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Climate change and health: what the Lancet Countdown says about the value and significance of local knowledge and action
Here is everything that the new Lancet Countdown says about the value and significance of indigenous and other forms of local knowledge, as well as their value for community-led action to respond to the impacts of climate change on health.
Why does this matter? Read our article: How the Lancet Countdown illuminates a new path to climate-resilient health systems
On the value of community-led action and the significance of local knowledge
Defining community-led action by its local context and empowerment
“Community-led actions are those spearheaded by self-organised individuals within a community, working together for a common goal. Rooted in local societal, cultural, and economic contexts, they can promote equity, empower local actors, and strengthen climate resilience.”
Community-led action as a driver of meaningful progress
“Individual, community-led, and civil society actions can drive meaningful progress with substantial health benefits.”
Grassroots activities growing into formal organizations
“These grassroots activities can grow into formal organisations with national or international influence.”
The dependence of community-led initiatives on local actors
“Despite their capacity to enact change, community-led initiatives depend on the willingness and possibilities of local actors.”
The advantages of community-led actions over top-down interventions
“Tailored to local needs, community-led actions are more likely than top-down interventions to maximise health benefits, bypass the limitations of implementing top-down solutions, and can help avoid unintended harms such as gentrification or increased inequalities.”
The co-benefits of community-led action on mental health and awareness
“Community-led actions can also foster agency, increase attachment to the local environment, and promote social interactions, all of which help reduce the mental health impacts of climate change and increase awareness.”
Recommendation for individuals and civil society: Engage in community-led action
“Engaging in community-led action on health and climate change, supporting equitable inclusion of marginalised communities.”
Recommendation for individuals and civil society: Create community platforms for collective resilience
“Creating community platforms on climate change and health, including citizen groups, to safely exchange ideas and concerns, build collective resilience and adaptive capacity, and enable engagement with decision makers.”
Value of local knowledge: We need more examples of community-led action
Example of local community and indigenous peoples’ forest management
“In Nepal, community forests user groups have grown into a state-sponsored and legally mandated initiative, under which local communities, including Indigenous Peoples, manage 37-7% of national forests—augmenting carbon sinks, enhancing food access, and improving livelihoods.”
Example of farmer-led interventions improving health outcomes
“Across the Sahel, farmers have implemented Farmer Managed Natural Regeneration… These farmer-led interventions resulted in increased tree coverage, crop yields, drought resistance, and access to traditional medicines, contributing to improved health outcome and poverty reduction.”
Environmental defenders need protection
The disproportionate killing of indigenous and minoritized environmental defenders
“A Global Witness report found that 196 activists were killed in 2023 (57% in Latin America), with minoritised and Indigenous groups disproportionately affected.”
Protecting environmental defenders to enable community-led interventions
“Protecting environmental defenders in line with international conventions is critical to enabling community-led interventions, and providing a fertile ground for grassroots initiatives to deliver life-saving progress on health and climate change.”
On the need for community-led action amid waning political engagement
The role of health framing in driving community-led action
“This [health framings of climate change] can be a crucial driver for individual-led and community-led action, especially amid waning engagement from political leaders.”
Community and individual action as essential when national engagement wanes
“When national government engagement wanes (indicator 5.4.1), action by subnational governments, corporations, civil society organisations, communities, and individuals can contribute to keeping the planet within inhabitable limits.”
Recommendation for funders on the significance of local knowledge:
Recommendation for funders: Support community initiatives to scale action
“…supporting governmental bodies, civil society organisations, and community initiatives to scale-up health-promoting and inclusive climate change action.”
On the value of indigenous knowledge
Respecting indigenous knowledge in global health action
“To support global health, these actions need to be delivered in ways that are gender-responsive, reduce health inequities, respect and promote the rights and knowledge of Indigenous People, and account for the protection of vulnerable and underserved communities.”
Recommendation for national governments: Integrate community and indigenous perspectives in policy design
“Including community perspectives in the design of climate and health policies, with particular focus on the most vulnerable communities and Indigenous people.”
Recommendation for city governments: Prioritize indigenous knowledge and community-led initiatives
“Reducing inequities and avoiding unintended harms by integrating community perspectives in all climate change actions and supporting community-led initiatives, with particular focus on vulnerable communities and the priorities and knowledge of Indigenous people.”
On the need to refocus the apparatus of science on the most vulnerable people and communities
Scientific evidence generation is concentrated in high-HDI countries, not where impacts are highest
“Scientific evidence generation is still concentrated in higher HDI countries rather than those most exposed to the health impacts of climate change.”
Data gaps obscuring the impacts on indigenous people
“This lack of disaggregated data makes it difficult to capture the disproportionate impacts of climate change on Indigenous people, such as those living in the circumpolar region, which is heating nearly four times faster than the global average.”
Conflict analysis must be shaped by local dynamics
“This relationship [between climate change and conflict] is now widely recognised as a complex, multicausal phenomenon shaped by local social and cultural dynamics, economic fluctuations, and geopolitical forces at both the domestic and international levels.”
On ensuring the relevance of science to support local action
Harnessing local knowledge for regional stakeholders
“…harnessing local knowledge and translating findings to meet the needs of local stakeholders.”
Advancing the local generation of evidence
“…to advance the local generation of evidence to inform action in one of the world’s most vulnerable regions.”
Informing action at the local level
“…make their findings available to inform action at the national and local levels.”
References
- Romanello, M., et al., 2025. The 2025 report of the Lancet Countdown on health and climate change. The Lancet S0140673625019191. https://doi.org/10.1016/S0140-6736(25)01919-1
- Sadki, R., 2024. Critical evidence gaps in the Lancet Countdown on health and climate change. https://doi.org/10.59350/nv6f2-svp12
Image: The Geneva Learning Foundation Collection © 2025
#communityResilience #communityLedAction #IndigenousKnowledge #LancetCountdown #localKnowledge #MarinaRomanello #The2025ReportOfTheLancetCountdownOnHealthAndClimateChange
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Climate change and health: what the Lancet Countdown says about the value and significance of local knowledge and action
Here is everything that the new Lancet Countdown says about the value and significance of indigenous and other forms of local knowledge, as well as their value for community-led action to respond to the impacts of climate change on health.
Why does this matter? Read our article: How the Lancet Countdown illuminates a new path to climate-resilient health systems
On the value of community-led action and the significance of local knowledge
Defining community-led action by its local context and empowerment
“Community-led actions are those spearheaded by self-organised individuals within a community, working together for a common goal. Rooted in local societal, cultural, and economic contexts, they can promote equity, empower local actors, and strengthen climate resilience.”
Community-led action as a driver of meaningful progress
“Individual, community-led, and civil society actions can drive meaningful progress with substantial health benefits.”
Grassroots activities growing into formal organizations
“These grassroots activities can grow into formal organisations with national or international influence.”
The dependence of community-led initiatives on local actors
“Despite their capacity to enact change, community-led initiatives depend on the willingness and possibilities of local actors.”
The advantages of community-led actions over top-down interventions
“Tailored to local needs, community-led actions are more likely than top-down interventions to maximise health benefits, bypass the limitations of implementing top-down solutions, and can help avoid unintended harms such as gentrification or increased inequalities.”
The co-benefits of community-led action on mental health and awareness
“Community-led actions can also foster agency, increase attachment to the local environment, and promote social interactions, all of which help reduce the mental health impacts of climate change and increase awareness.”
Recommendation for individuals and civil society: Engage in community-led action
“Engaging in community-led action on health and climate change, supporting equitable inclusion of marginalised communities.”
Recommendation for individuals and civil society: Create community platforms for collective resilience
“Creating community platforms on climate change and health, including citizen groups, to safely exchange ideas and concerns, build collective resilience and adaptive capacity, and enable engagement with decision makers.”
Value of local knowledge: We need more examples of community-led action
Example of local community and indigenous peoples’ forest management
“In Nepal, community forests user groups have grown into a state-sponsored and legally mandated initiative, under which local communities, including Indigenous Peoples, manage 37-7% of national forests—augmenting carbon sinks, enhancing food access, and improving livelihoods.”
Example of farmer-led interventions improving health outcomes
“Across the Sahel, farmers have implemented Farmer Managed Natural Regeneration… These farmer-led interventions resulted in increased tree coverage, crop yields, drought resistance, and access to traditional medicines, contributing to improved health outcome and poverty reduction.”
Environmental defenders need protection
The disproportionate killing of indigenous and minoritized environmental defenders
“A Global Witness report found that 196 activists were killed in 2023 (57% in Latin America), with minoritised and Indigenous groups disproportionately affected.”
Protecting environmental defenders to enable community-led interventions
“Protecting environmental defenders in line with international conventions is critical to enabling community-led interventions, and providing a fertile ground for grassroots initiatives to deliver life-saving progress on health and climate change.”
On the need for community-led action amid waning political engagement
The role of health framing in driving community-led action
“This [health framings of climate change] can be a crucial driver for individual-led and community-led action, especially amid waning engagement from political leaders.”
Community and individual action as essential when national engagement wanes
“When national government engagement wanes (indicator 5.4.1), action by subnational governments, corporations, civil society organisations, communities, and individuals can contribute to keeping the planet within inhabitable limits.”
Recommendation for funders on the significance of local knowledge:
Recommendation for funders: Support community initiatives to scale action
“…supporting governmental bodies, civil society organisations, and community initiatives to scale-up health-promoting and inclusive climate change action.”
On the value of indigenous knowledge
Respecting indigenous knowledge in global health action
“To support global health, these actions need to be delivered in ways that are gender-responsive, reduce health inequities, respect and promote the rights and knowledge of Indigenous People, and account for the protection of vulnerable and underserved communities.”
Recommendation for national governments: Integrate community and indigenous perspectives in policy design
“Including community perspectives in the design of climate and health policies, with particular focus on the most vulnerable communities and Indigenous people.”
Recommendation for city governments: Prioritize indigenous knowledge and community-led initiatives
“Reducing inequities and avoiding unintended harms by integrating community perspectives in all climate change actions and supporting community-led initiatives, with particular focus on vulnerable communities and the priorities and knowledge of Indigenous people.”
On the need to refocus the apparatus of science on the most vulnerable people and communities
Scientific evidence generation is concentrated in high-HDI countries, not where impacts are highest
“Scientific evidence generation is still concentrated in higher HDI countries rather than those most exposed to the health impacts of climate change.”
Data gaps obscuring the impacts on indigenous people
“This lack of disaggregated data makes it difficult to capture the disproportionate impacts of climate change on Indigenous people, such as those living in the circumpolar region, which is heating nearly four times faster than the global average.”
Conflict analysis must be shaped by local dynamics
“This relationship [between climate change and conflict] is now widely recognised as a complex, multicausal phenomenon shaped by local social and cultural dynamics, economic fluctuations, and geopolitical forces at both the domestic and international levels.”
On ensuring the relevance of science to support local action
Harnessing local knowledge for regional stakeholders
“…harnessing local knowledge and translating findings to meet the needs of local stakeholders.”
Advancing the local generation of evidence
“…to advance the local generation of evidence to inform action in one of the world’s most vulnerable regions.”
Informing action at the local level
“…make their findings available to inform action at the national and local levels.”
References
- Romanello, M., et al., 2025. The 2025 report of the Lancet Countdown on health and climate change. The Lancet S0140673625019191. https://doi.org/10.1016/S0140-6736(25)01919-1
- Sadki, R., 2024. Critical evidence gaps in the Lancet Countdown on health and climate change. https://doi.org/10.59350/nv6f2-svp12
Image: The Geneva Learning Foundation Collection © 2025
#communityResilience #communityLedAction #IndigenousKnowledge #LancetCountdown #localKnowledge #MarinaRomanello #The2025ReportOfTheLancetCountdownOnHealthAndClimateChange
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Gender in emergencies: a new peer learning programme from The Geneva Learning Foundation
This is a critical moment for work on gender in emergencies.
Across the humanitarian sector, we are witnessing a coordinated backlash.
Decades of progress are threatened by targeted funding cuts, the erasure of essential research and tools, and a political climate that seeks to silence our work.
Many dedicated practitioners feel isolated and that their work is being devalued.
This is not a time for silence.
It is a time for solidarity and for finding resilient ways to sustain our practice.
In this spirit, The Geneva Learning Foundation is pleased to announce the new Certificate peer learning programme for gender in emergencies.
We offer this programme to build upon the decades of vital work by countless practitioners and activists, seeing our role as one of contribution to the collective effort of all who continue to champion gender equality in emergencies.
Learn more and request your invitation to the programme and its first course here.
Our approach: A programme built from the ground up
This programme was built from scratch with a distinct philosophy.
We did not start with a pre-packaged curriculum.
Instead, we turned to two foundational sources of knowledge.
- First, we listened to the most valuable resource we have: the firsthand experiences of thousands of practitioners in our global network. Their stories of what truly happens on the front lines—what works, what fails, and why—form the living heart of this programme.
- Second, we grounded our approach in the deep insights of intersectional, decolonial, and feminist scholarship. These perspectives challenge us to move beyond technical fixes and to analyze the systems of power that create gender inequality in the first place.
This unique origin means our programme is a dynamic space co-created with and for practitioners who are serious about transformative change.
Gender in emergencies: Gender through an intersectional lens
Our focus is squarely on gender in emergencies.
We start with gender analysis because it is a fundamental tool for effective humanitarian action.
However, we use an intersectional lens.
We recognize that a person’s experience is shaped not by gender alone, but by how their gender compounds with their age, disability, ethnicity, and other aspects of their identity.
This lens does not replace gender analysis.
It makes it stronger.
It allows us to see how power works differently for different women, men, girls, and boys, and helps us to design solutions that do not inadvertently leave behind the people marginalized by something other than their gender.
Gender in emergencies requires learning at the speed of crisis
Humanitarian response must be rapid, and so must our learning.
A slow, top-down training model cannot keep pace with the reality of a crisis.
The Geneva Learning Foundation’s Impact Accelerator is a peer learning-to-action model built for the speed and complexity of humanitarian settings.
It is a ‘learn-by-doing’ experience where your frontline experience is the textbook.
The model is designed to quickly turn your individual insights into collective knowledge and practical action.
You analyze a real challenge from your work, share it with a small group of global peers, and use their feedback to build a concrete plan.
This process accelerates the development of context-specific solutions that are grounded in reality, not just theory.
Your first step: The foundational primer for gender in emergencies
We are starting this new programme with a free, open-access foundational course.
Enrollment is now open.
The course is a quick primer that introduces core concepts of gender, intersectionality, and bias through the real-world stories of practitioners.
It provides the shared language and practical tools to begin your journey of reflection, peer collaboration, and action.
Building a resilient community
This is more than a training programme.
It is an invitation to join a global community of practice.
In a time of backlash and division, creating spaces where we can learn from each other, share our struggles, and find solidarity is a critical act of resistance.
If you are ready to deepen your practice and connect with colleagues who share your commitment, we invite you to join us.
Image: The Geneva Learning Foundation © 2025
#CertificatePeerLearningProgrammeForGenderInEmergencies #climateAndHealth #GenderInEmergencies #genderLens #globalHealth #humanitarianResponse #peerLearning #RapidGenderAnalysis #RGA #TheGenevaLearningFoundation -
Gender in emergencies: a new peer learning programme from The Geneva Learning Foundation
This is a critical moment for work on gender in emergencies.
Across the humanitarian sector, we are witnessing a coordinated backlash.
Decades of progress are threatened by targeted funding cuts, the erasure of essential research and tools, and a political climate that seeks to silence our work.
Many dedicated practitioners feel isolated and that their work is being devalued.
This is not a time for silence.
It is a time for solidarity and for finding resilient ways to sustain our practice.
In this spirit, The Geneva Learning Foundation is pleased to announce the new Certificate peer learning programme for gender in emergencies.
We offer this programme to build upon the decades of vital work by countless practitioners and activists, seeing our role as one of contribution to the collective effort of all who continue to champion gender equality in emergencies.
Learn more and request your invitation to the programme and its first course here.
Our approach: A programme built from the ground up
This programme was built from scratch with a distinct philosophy.
We did not start with a pre-packaged curriculum.
Instead, we turned to two foundational sources of knowledge.
- First, we listened to the most valuable resource we have: the firsthand experiences of thousands of practitioners in our global network. Their stories of what truly happens on the front lines—what works, what fails, and why—form the living heart of this programme.
- Second, we grounded our approach in the deep insights of intersectional, decolonial, and feminist scholarship. These perspectives challenge us to move beyond technical fixes and to analyze the systems of power that create gender inequality in the first place.
This unique origin means our programme is a dynamic space co-created with and for practitioners who are serious about transformative change.
Gender in emergencies: Gender through an intersectional lens
Our focus is squarely on gender in emergencies.
We start with gender analysis because it is a fundamental tool for effective humanitarian action.
However, we use an intersectional lens.
We recognize that a person’s experience is shaped not by gender alone, but by how their gender compounds with their age, disability, ethnicity, and other aspects of their identity.
This lens does not replace gender analysis.
It makes it stronger.
It allows us to see how power works differently for different women, men, girls, and boys, and helps us to design solutions that do not inadvertently leave behind the people marginalized by something other than their gender.
Gender in emergencies requires learning at the speed of crisis
Humanitarian response must be rapid, and so must our learning.
A slow, top-down training model cannot keep pace with the reality of a crisis.
The Geneva Learning Foundation’s Impact Accelerator is a peer learning-to-action model built for the speed and complexity of humanitarian settings.
It is a ‘learn-by-doing’ experience where your frontline experience is the textbook.
The model is designed to quickly turn your individual insights into collective knowledge and practical action.
You analyze a real challenge from your work, share it with a small group of global peers, and use their feedback to build a concrete plan.
This process accelerates the development of context-specific solutions that are grounded in reality, not just theory.
Your first step: The foundational primer for gender in emergencies
We are starting this new programme with a free, open-access foundational course.
Enrollment is now open.
The course is a quick primer that introduces core concepts of gender, intersectionality, and bias through the real-world stories of practitioners.
It provides the shared language and practical tools to begin your journey of reflection, peer collaboration, and action.
Building a resilient community
This is more than a training programme.
It is an invitation to join a global community of practice.
In a time of backlash and division, creating spaces where we can learn from each other, share our struggles, and find solidarity is a critical act of resistance.
If you are ready to deepen your practice and connect with colleagues who share your commitment, we invite you to join us.
Image: The Geneva Learning Foundation © 2025
#CertificatePeerLearningProgrammeForGenderInEmergencies #climateAndHealth #GenderInEmergencies #genderLens #globalHealth #humanitarianResponse #peerLearning #RapidGenderAnalysis #RGA #TheGenevaLearningFoundation -
The crisis in scientific publishing: from AI fraud to epistemic injustice
There is a crisis in scientific publishing. Science is haunted. In early 2024, one major publisher retracted hundreds of scientific papers. Most were not the work of hurried researchers, but of ghosts: digital phantoms generated by artificial intelligence. Featuring nonsensical diagrams and fabricated data, they had sailed through the gates of peer review.
This spectre of AI-driven fraud is not only a new technological threat. It is also a symptom of a pre-existing disease. For years, organized networks have profited from inserting fake papers into the scholarly record. It seems that scientific publishing’s peer review process, intended to seek truth, cannot even tell the real from the fake.
These failures are not just academic embarrassments. In fields like global health, where knowledge means the difference between life and death, we can no longer afford to ignore them. Indeed, the crisis in scientific journals is not, at its heart, a crisis in publishing. It is a crisis of knowledge—of what we value, who we trust, and how we come to know. That makes it a crisis of education.
Crisis in scientific publishing: The knowledge we ignore
Consider what Toby Green has called the “dark side of the moon.” He is referring to the vast body of knowledge produced by established experts in international organizations. Volumes of high-quality reports and analyses come from organizations large and small. They contain immense expertise. Often, not only do they qualify as science. They may be more likely to shape policy and practice than most academic outputs. Yet this “grey literature” is rarely incorporated into the scholarly record. This is why Green is actively implementing projects to find, collect, and index such materials.
If the formal knowledge of some of the world’s leading experts is being left in the dark, what hope is there for the practical wisdom of a frontline nurse?
In the rigid hierarchy of evidence that governs global health, a randomized controlled trial sits at the pinnacle. At the very bottom, dismissed as mere “anecdotes,” lies the lived experience of practitioners. A nurse in a rural clinic who discovers a better way to dress a wound in a humid environment has generated life-saving knowledge that could be useful elsewhere. A community health worker who develops a sophisticated method for building trust with vaccine-hesitant parents has solved a problem in context. Yet, in our current culture, their insights are not data. Their experience is not evidence.
To dismiss such knowledge is an act of willful ignorance. Science, at its best, is a process of disciplined curiosity. Its fundamental purpose is to reduce ignorance and expand our understanding of the world. To willfully ignore entire categories of human experience and expertise is therefore a betrayal of the scientific ethos itself. It is an active choice to remain in the dark.
Crisis in scientific publishing: the architecture of exclusion
This devaluation of practical knowledge is not an accident. It is a feature of a system designed to exclude. The modern ideal of science began with a radically open mission. As the scholar John Willinsky has meticulously documented in his history of Western European science, the creation of scientific journals in the 17th century was intended to create a public commons of knowledge, accelerating progress for the benefit of humanity. The principle was one of access. How was this mission corrupted?
The architecture of modern science was built on a colonial foundation. Its violence was not only physical but also scientific and intellectual. Frantz Fanon, the Martinican psychiatrist who became a theorist of decolonization in the crucible of Algeria’s war of independence, described colonization’s deepest work as the effort to “empty the mind of the colonized.” This is a systematic process of convincing people that our own histories, cultures, and ways of knowing are worthless.
Generations later, the Māori scholar Linda Tuhiwai Smith detailed how this was put into practice. She showed that Western research methodologies themselves were often not neutral tools of discovery but instruments of empire. The acts of observing, classifying, extracting, and analyzing were used to control populations and invalidate their knowledge systems, replacing them with a single, supposedly universal, European model of truth.
This worldview pretends to be a neutral, “view from nowhere,” a concept also critiqued powerfully by the white American feminist philosopher Donna Haraway. She argued that all knowledge is situated—shaped by the position and perspective of the knower. You see the landscape differently from the mountain top than you do from the valley. A complete map requires both perspectives.
Echoing this, her philosophical and geographical sister Sandra Harding argued that by excluding the perspectives of marginalized people, dominant science becomes “weakly objective.” It is blind to its own biases and assumptions.
Crisis in scientific publishing: Fear of knowledge
A common and deeply felt fear among scientists is that embracing diverse forms of knowledge will lead to a dangerous relativism, where objective truth dissolves and “anything goes.”
Harding’s work shows this fear to be misplaced. She argues that the “view from nowhere” provides not a stronger, but a more brittle and fragile grasp of the truth. A truly “strong objectivity,” she contended, is achieved by intentionally seeking out multiple, situated perspectives. This does not mean that all views are equally valid. It means that by examining a problem from many standpoints, we can triangulate a more robust and reliable understanding of reality. We can identify the biases and blind spots inherent in any single view, including our own.
This process is the antidote to the willful ignorance mentioned earlier. It strengthens our grasp of objective truth by making it more complete and more honest.
Can change be paved by good intentions?
Today, the need for a change in research culture is widely acknowledged. The world’s largest research funders publish reports calling for more diversity and inclusion. Yet we observe paralysis rather than progress. The individuals who sit on the decision-making committees of such institutions will almost certainly not fund a project with a primary investigator whose work is not validated by the existing system of prestigious but exclusive journals. Elite global scholars leading the vital movement to “decolonize global health” first established their legitimacy by adhering to conventional norms, then began using the master’s tools to have their critiques of the system heard. Such contradictions illustrate how deeply the exclusionary norms are embedded.
Since top-down change is caught in such contradictions, a meaningful path forward may be to change the culture of science from the ground up. The core challenge is to correct for epistemic injustice: the wrong done to someone in their capacity as a knower. This injustice takes several insidious forms.
The most obvious is testimonial injustice. Imagine the scene. A senior male doctor from a famous university presents a finding and is met with nods of assent. His words carry the weight of evidence. A young female nurse from a rural clinic presents the exact same finding based on her direct experience. Her knowledge is dismissed as a “story” or an “anecdote.” She is not heard because of who she is. Her credibility is unjustly discounted.
Even deeper is hermeneutical injustice. This is the wrong of not even having the shared language to make your experience understood by the dominant culture. The community health worker who builds trust with hesitant parents may have a brilliant system, but if they cannot articulate it in the formal jargon of “implementation science,” their knowledge remains invisible. They are wronged not because they are disbelieved. They are wronged because the system lacks the concepts to even recognize their wisdom as knowledge in the first place.
Projects like Toby Green’s grey literature repository or initiatives like Rogue Scholar, pioneered by Martin Fenner, that assign a permanent Digital Object Identifier (DOI) to science that was not previously in the scholarly record, are practical interventions. But this not a technological problem. It is an educational one. Changing a culture that perpetuates these injustices is the primary work. Within this larger project, new tools can serve as tactics of resistance. As such, they can be used to support acts of epistemic defiance, for example by creating a formal, citable record of knowledge that exists outside the traditional gates. Yet they remain tools, not the solution.
The science of knowing
You cannot fix a broken culture by patching its systems. You must change its DNA. The crisis haunting science is not ultimately about publishing, fraud, or peer review. It is a crisis of education—not of schooling, but of how we come to know. If physics is the science of matter, education is the science of all sciences. It provides the architecture of assumptions and values that shapes how every other field discovers and validates truth.
A new philosophy of education is needed, one that includes these three principles:
- It must recognize that the most durable knowledge comes from praxis—the cycle of acting in the world and reflecting on the consequences.
- It must be built on collaborative intelligence, understanding that the most difficult problems can only be solved by weaving together many perspectives.
- It must pursue strong objectivity, not by erasing human perspective, but by intentionally seeking it out to create a more complete and honest picture of reality.
To change science, we must change how scientists are taught to see the world. We must educate for humility, for critical self-awareness, and for the ability to listen. This is the work of creating a science that is not haunted by its failures but is directly contributes to a more just and truthful account of our world.
References
- Boghossian, P., 2007. Fear of knowledge: Against relativism and constructivism. Clarendon Press.
- Couch, L., 2021. Wellcome Diversity, equity and inclusion strategy [WWW Document]. Wellcome. URL https://wellcome.org/what-we-do/diversity-and-inclusion/strategy (accessed 11.8.22).
- Fanon, F. (1963). The wretched of the earth. Grove Press.
- Fenner, M., 2023. The Rogue Scholar: An Archive for Scholarly blogs. Upstream. https://doi.org/10.54900/bj4g7p2-2f0fn9b
- Gitau, E., Khisa, A., Vicente-Crespo, M., Sengor, D., Otoigo, L., Ndong, C., Simiyu, A., 2023. African Research Culture – Opinion Research. African Population and Health Research Center, Nairobi, Kenya. https://aphrc.org/project/african-research-culture-opinion-research/
- Green, T., 2022. Wait! What? There’s stuff missing from the scholarly record? Med Writ 31, 44–48. https://doi.org/10.56012/ajel9043
- Haraway, D. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575–599. https://doi.org/10.2307/3178066
- Harding, S. (1991). Whose science? Whose knowledge? Thinking from women’s lives. Cornell University Press.
- Smith, L. T. (2012). Decolonizing methodologies: Research and indigenous peoples (2nd ed.). Zed Books.
- The Social Investment Consultancy, The Better Org, Cole, N., Cole, L., 2022. Evaluation of Wellcome Anti-Racism Programme Final Evaluation Report – Public. Wellcome, London. https://cms.wellcome.org/sites/default/files/2022-08/Evaluation-of-Wellcome-Anti-Racism-Programme-Final-Evaluation-Report-2022.pdf
- Wellcome Trust, 2020. What researchers think about the culture they work in. Wellcome, London. https://wellcome.org/reports/what-researchers-think-about-research-culture
- Willinsky, J., 2006. The access principle: The case for open access to research and scholarship. MIT press Cambridge, MA.
Image: The Geneva Learning Foundation Collection © 2025
#AIFraud #DEI #diversityAndInclusion #DonnaHaraway #epistemicInjustice #epistemology #hermeneuticalInjustice #LindaTuhiwaiSmith #philosophyOfScience #RogueScholar #SandraHarding #scientificPublishing #strongObjectivity #testimonialInjustice #TobyGreen -
Richard Mayer’s research on multimedia for learning actually proves text works better
Educational technology professionals cite Richard Mayer’s 2008 study more than any other research on multimedia instruction.
They are citing the wrong conclusion.
Mayer did not prove multimedia enhances learning.
He proved multimedia creates cognitive problems requiring ten different workarounds – and accidentally built the case for text-based instruction.
What Richard Mayer actually found
Through hundreds of controlled experiments, Richard Mayer identified ten principles for multimedia design.
The pattern is striking: most principles involve removing elements from presentations.
Five principles focus on reducing “extraneous processing” – cognitive waste that multimedia creates.
- Remove irrelevant material.
- Highlight essential information buried among distractions.
- Eliminate simultaneous animation, narration, and text because learners perform better with only two elements.
- Place corresponding words and pictures close together.
- Present them simultaneously, not sequentially.
Three principles manage “essential processing” when content is complex.
- Break presentations into learner-controlled segments.
- Use spoken rather than printed text with graphics.
- Provide pre-training before complex multimedia instruction.
Two principles foster deeper learning.
- Combine words and pictures rather than words alone.
- Use conversational rather than formal language.
The hidden message: multimedia instruction is so cognitively demanding that it requires ten specialized principles to avoid harming learning.
Richard Mayer’s split attention revelation
Mayer’s modality principle seems to endorse multimedia: learners perform better with graphics plus spoken text than graphics plus printed text.
Educational technologists celebrate this as proof that multimedia works.
They miss the real insight.
Graphics with printed text create split attention – learners cannot simultaneously look at pictures while reading words.
They must constantly switch between visual elements, wasting cognitive resources on coordination rather than learning.
Richard Mayer’s solution uses different channels: visual graphics with auditory narration.
But this still requires complex mental coordination between multiple input streams while maintaining focus on learning objectives.
Text-based instruction eliminates split attention entirely.
(There are deeply-rooted cultural and historical reasons for the distrust of text.)
Learners process information through one coherent channel that naturally supports sequential, analytical thinking.
The damage control principles in Richard Mayer’s principles
Step back from individual findings and Mayer’s principles reveal themselves as damage control.
The coherence principle removes distractions that multimedia introduces.
The redundancy principle eliminates conflicts between competing inputs.
The segmenting principle provides control that multimedia complexity demands.
The pre-training principle prepares learners for cognitive challenges that simpler instruction avoids.
Each principle represents additional design constraints requiring specialized expertise and extensive testing.
They exist because multimedia instruction is fundamentally problematic.
Text extends Richard Mayer’s logic
At The Geneva Learning Foundation, we work with 70,000 health practitioners using text-based peer learning.
Nigerian practitioners write about extreme heat forcing people to sleep outdoors, increasing malaria exposure.
Colleagues in Brazil, Chad, Ghana, and India read these accounts, analyze climate-health connections, and provide structured feedback through expert-designed rubrics.
No graphics.
No audio coordination.
No split attention problems.
Read our article: Against chocolate-covered broccoli: text-based alternatives to expensive multimedia content
Direct engagement with content that supports rather than complicates learning.
This approach achieves Richard Mayer’s goals through elimination rather than optimization.
Ultimate coherence by presenting only essential information.
Zero redundancy through single-channel processing.
Natural segmenting through text’s inherent reader control.
No pre-training needed because text presents information in logical, sequential structures.
The multimedia principle reconsidered
Mayer’s most famous finding – people learn better from words and pictures than words alone – deserves scrutiny.
This emerged from comparing passive multimedia consumption to passive text reading.
It equates learning with recall.
Neither condition included structured peer interaction, collaborative analysis, or iterative revision that characterize more complex learning.
When learners create knowledge through text-based peer learning, they achieve outcomes that passive consumption of any media cannot match.
The effect size for active text-based learning exceeds Mayer’s multimedia findings while avoiding cognitive coordination problems.
The economic evidence
Mayer’s ten principles exist because multimedia design is expensive and complex.
Each principle represents additional constraints demanding specialized expertise.
Typical multimedia modules are expensive.
Text-based peer learning costs a fraction of this amount while producing superior outcomes.
Resources should flow toward learning infrastructure such as expert rubrics and facilitated dialogue – elements that actually drive learning rather than manage cognitive problems.
The real choice
Educational technology leaders face a fundamental decision: invest in managing multimedia’s problems or adopt approaches that avoid those problems entirely.
Mayer’s research illuminates multimedia’s cognitive costs.
His ten principles represent sophisticated damage control, not learning enhancement.
They minimize harm rather than maximize potential.
Text-based instruction honors Mayer’s deeper insights while rejecting surface implications.
It achieves the cognitive efficiency his principles attempt to restore to multimedia environments.
References
- Berrocal, Y., Regan, J., Fisher, J., Darr, A., Hammersmith, L., Aiyer, M., 2021. Implementing Rubric-Based Peer Review for Video Microlecture Design in Health Professions Education. Med.Sci.Educ. 31, 1761–1765. https://doi.org/10.1007/s40670-021-01437-1
- Clark, R.C., Mayer, R.E. (Eds.), 2016. e‐Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning, 1st ed. Wiley. https://doi.org/10.1002/9781119239086
- Feenberg, A. The written world: On the theory and practice of computer conferencing. Mindweave: Communication, computers, and distance education 22–39 (1989).
- Mayer, R.E., 2008. Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American Psychologist 63, 760–769. https://doi.org/10.1037/0003-066X.63.8.760
- Mayer, R.E., 2005. Cognitive Theory of Multimedia Learning, in: Mayer, R. (Ed.), The Cambridge Handbook of Multimedia Learning. Cambridge University Press, pp. 31–48. https://doi.org/10.1017/CBO9780511816819.004
- Mayer, R.E., Heiser, J., Lonn, S., 2001. Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93, 187–198. https://doi.org/10.1037/0022-0663.93.1.187
- Mayer, R.E., Moreno, R., 2003. Nine Ways to Reduce Cognitive Load in Multimedia Learning. Educational Psychologist 38, 43–52. https://doi.org/10.1207/S15326985EP3801_6
- Mayer, R.E., Moreno, R., 2002. Animation as an Aid to Multimedia Learning. Educational Psychology Review 14, 87–99. https://doi.org/10.1023/A:1013184611077
- Plass, J.L., Chun, D.M., Mayer, R.E., Leutner, D., 2003. Cognitive load in reading a foreign language text with multimedia aids and the influence of verbal and spatial abilities. Computers in Human Behavior 19, 221–243. https://doi.org/10.1016/S0747-5632(02)00015-8
- Sweller, J., 2005. Implications of Cognitive Load Theory for Multimedia Learning, in: Mayer, R. (Ed.), The Cambridge Handbook of Multimedia Learning. Cambridge University Press, pp. 19–30. https://doi.org/10.1017/CBO9780511816819.003
Image: The Geneva Learning Foundation Collection © 2025
#cognitiveLoad #CognitiveLoadTheory #eLearning #instruction #learning #multimedia #multimediaLearning #RichardMayer #text -
Richard Mayer’s research on multimedia for learning actually proves text works better
Educational technology professionals cite Richard Mayer’s 2008 study more than any other research on multimedia instruction.
They are citing the wrong conclusion.
Mayer did not prove multimedia enhances learning.
He proved multimedia creates cognitive problems requiring ten different workarounds – and accidentally built the case for text-based instruction.
What Richard Mayer actually found
Through hundreds of controlled experiments, Richard Mayer identified ten principles for multimedia design.
The pattern is striking: most principles involve removing elements from presentations.
Five principles focus on reducing “extraneous processing” – cognitive waste that multimedia creates.
- Remove irrelevant material.
- Highlight essential information buried among distractions.
- Eliminate simultaneous animation, narration, and text because learners perform better with only two elements.
- Place corresponding words and pictures close together.
- Present them simultaneously, not sequentially.
Three principles manage “essential processing” when content is complex.
- Break presentations into learner-controlled segments.
- Use spoken rather than printed text with graphics.
- Provide pre-training before complex multimedia instruction.
Two principles foster deeper learning.
- Combine words and pictures rather than words alone.
- Use conversational rather than formal language.
The hidden message: multimedia instruction is so cognitively demanding that it requires ten specialized principles to avoid harming learning.
Richard Mayer’s split attention revelation
Mayer’s modality principle seems to endorse multimedia: learners perform better with graphics plus spoken text than graphics plus printed text.
Educational technologists celebrate this as proof that multimedia works.
They miss the real insight.
Graphics with printed text create split attention – learners cannot simultaneously look at pictures while reading words.
They must constantly switch between visual elements, wasting cognitive resources on coordination rather than learning.
Richard Mayer’s solution uses different channels: visual graphics with auditory narration.
But this still requires complex mental coordination between multiple input streams while maintaining focus on learning objectives.
Text-based instruction eliminates split attention entirely.
(There are deeply-rooted cultural and historical reasons for the distrust of text.)
Learners process information through one coherent channel that naturally supports sequential, analytical thinking.
The damage control principles in Richard Mayer’s principles
Step back from individual findings and Mayer’s principles reveal themselves as damage control.
The coherence principle removes distractions that multimedia introduces.
The redundancy principle eliminates conflicts between competing inputs.
The segmenting principle provides control that multimedia complexity demands.
The pre-training principle prepares learners for cognitive challenges that simpler instruction avoids.
Each principle represents additional design constraints requiring specialized expertise and extensive testing.
They exist because multimedia instruction is fundamentally problematic.
Text extends Richard Mayer’s logic
At The Geneva Learning Foundation, we work with 70,000 health practitioners using text-based peer learning.
Nigerian practitioners write about extreme heat forcing people to sleep outdoors, increasing malaria exposure.
Colleagues in Brazil, Chad, Ghana, and India read these accounts, analyze climate-health connections, and provide structured feedback through expert-designed rubrics.
No graphics.
No audio coordination.
No split attention problems.
Read our article: Against chocolate-covered broccoli: text-based alternatives to expensive multimedia content
Direct engagement with content that supports rather than complicates learning.
This approach achieves Richard Mayer’s goals through elimination rather than optimization.
Ultimate coherence by presenting only essential information.
Zero redundancy through single-channel processing.
Natural segmenting through text’s inherent reader control.
No pre-training needed because text presents information in logical, sequential structures.
The multimedia principle reconsidered
Mayer’s most famous finding – people learn better from words and pictures than words alone – deserves scrutiny.
This emerged from comparing passive multimedia consumption to passive text reading.
It equates learning with recall.
Neither condition included structured peer interaction, collaborative analysis, or iterative revision that characterize more complex learning.
When learners create knowledge through text-based peer learning, they achieve outcomes that passive consumption of any media cannot match.
The effect size for active text-based learning exceeds Mayer’s multimedia findings while avoiding cognitive coordination problems.
The economic evidence
Mayer’s ten principles exist because multimedia design is expensive and complex.
Each principle represents additional constraints demanding specialized expertise.
Typical multimedia modules are expensive.
Text-based peer learning costs a fraction of this amount while producing superior outcomes.
Resources should flow toward learning infrastructure such as expert rubrics and facilitated dialogue – elements that actually drive learning rather than manage cognitive problems.
The real choice
Educational technology leaders face a fundamental decision: invest in managing multimedia’s problems or adopt approaches that avoid those problems entirely.
Mayer’s research illuminates multimedia’s cognitive costs.
His ten principles represent sophisticated damage control, not learning enhancement.
They minimize harm rather than maximize potential.
Text-based instruction honors Mayer’s deeper insights while rejecting surface implications.
It achieves the cognitive efficiency his principles attempt to restore to multimedia environments.
References
- Berrocal, Y., Regan, J., Fisher, J., Darr, A., Hammersmith, L., Aiyer, M., 2021. Implementing Rubric-Based Peer Review for Video Microlecture Design in Health Professions Education. Med.Sci.Educ. 31, 1761–1765. https://doi.org/10.1007/s40670-021-01437-1
- Clark, R.C., Mayer, R.E. (Eds.), 2016. e‐Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning, 1st ed. Wiley. https://doi.org/10.1002/9781119239086
- Feenberg, A. The written world: On the theory and practice of computer conferencing. Mindweave: Communication, computers, and distance education 22–39 (1989).
- Mayer, R.E., 2008. Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American Psychologist 63, 760–769. https://doi.org/10.1037/0003-066X.63.8.760
- Mayer, R.E., 2005. Cognitive Theory of Multimedia Learning, in: Mayer, R. (Ed.), The Cambridge Handbook of Multimedia Learning. Cambridge University Press, pp. 31–48. https://doi.org/10.1017/CBO9780511816819.004
- Mayer, R.E., Heiser, J., Lonn, S., 2001. Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93, 187–198. https://doi.org/10.1037/0022-0663.93.1.187
- Mayer, R.E., Moreno, R., 2003. Nine Ways to Reduce Cognitive Load in Multimedia Learning. Educational Psychologist 38, 43–52. https://doi.org/10.1207/S15326985EP3801_6
- Mayer, R.E., Moreno, R., 2002. Animation as an Aid to Multimedia Learning. Educational Psychology Review 14, 87–99. https://doi.org/10.1023/A:1013184611077
- Plass, J.L., Chun, D.M., Mayer, R.E., Leutner, D., 2003. Cognitive load in reading a foreign language text with multimedia aids and the influence of verbal and spatial abilities. Computers in Human Behavior 19, 221–243. https://doi.org/10.1016/S0747-5632(02)00015-8
- Sweller, J., 2005. Implications of Cognitive Load Theory for Multimedia Learning, in: Mayer, R. (Ed.), The Cambridge Handbook of Multimedia Learning. Cambridge University Press, pp. 19–30. https://doi.org/10.1017/CBO9780511816819.003
Image: The Geneva Learning Foundation Collection © 2025
#cognitiveLoad #CognitiveLoadTheory #eLearning #instruction #learning #multimedia #multimediaLearning #RichardMayer #text -
Climate change and health: a new peer learning programme by and for health workers from the most climate-vulnerable countries
GENEVA, Switzerland, 23 July 2025 (The Geneva Learning Foundation) –Today, The Geneva Learning Foundation (TGLF) announces the launch of “Learning to lead change on the frontline of climate change and health,” the inaugural course in a new certificate programme designed by and for professionals facing climate change impacts on health.
Enrollment is now open. The course will launch on 11 August 2025.
- English: https://go.learning.foundation/tglf/c/20132
- French: https://go.learning.foundation/tglf/c/21450
Two years ago today, nearly 5,000 health professionals from across the developing world gathered online for an unprecedented conversation. They shared something most climate scientists had never heard: detailed, firsthand accounts of how rising temperatures, extreme weather, and environmental changes were already devastating the health of their communities.
The stories were urgent and specific. A nurse in Ghana described managing surges of malaria after unprecedented flooding. A community health worker in Bangladesh explained how cholera outbreaks followed every major storm. A pharmacist in Nigeria watched children suffer malnutrition as crops failed during extended droughts.
“I can hear the worry in your voices,” one global health partner told participants during those historic July 2023 events, “and I really respect the time that you are giving to tell us about what is happening to you directly.”
Connecting the dots from individual impact to systemic crisis
While climate change dominates headlines for its environmental and economic impacts, a parallel health crisis has been quietly unfolding in clinics and hospitals across Africa, Asia, and Latin America. Health workers have become first-hand witnesses to climate change’s human toll.
Dr. Seydou Mohamed Ouedraogo from Burkina Faso described devastating floods that “really marked the memory of the inhabitants” and led to cascading health impacts.
Felix Kole from Gambia reported that “wells have turned to salty water” due to rising sea levels, while extreme heat meant “people are no longer sleeping inside their houses,” creating new security and health complications.
Rebecca Akello, a public health nurse from Uganda, documented malnutrition impacts directly: “During dry spells where there is no food, children come and their growth monitoring shows they really score low weight for age.”
Health professionals like Dr. Iktiyar Kandaker from Bangladesh already get that this is a systemic challenge: “Our health system is not prepared to actually address these situations. So this is a combined challenge… but it requires a lot of time to fix it.”
These health workers serve as what TGLF calls “trusted advisors”—over half describe themselves as being like “members of the family” to the populations they serve. Yet until now, they have had no structured way to learn from each other’s experiences or develop coordinated responses to climate health challenges.
Learning from those who know because they are there every day
“It is something that all of us have to join hands to be able to do the most we can to educate our communities on what they can do,” said Monica Agu, a community pharmacist from Nigeria who participated in the founding 2023 events. Her words captured the collaborative spirit that has driven the programme’s development.
The new certificate programme employs TGLF’s proven peer learning methodology, recognizing that health workers are already implementing life-saving climate adaptations with limited resources. During the 2023 events, participants shared examples of modified immunization schedules during heat waves, cholera outbreak management after flooding, and maintaining health services during extreme weather events.
“We believe that investing in health workers is one of the best ways to accelerate and strengthen the response to climate change impacts on health,” explains TGLF Executive Director Reda Sadki.
The programme has been developed from comprehensive analysis of health worker experiences documented since 2023. Most observations come from small and medium-sized communities in the most climate-vulnerable countries.
For health, a different kind of climate action
Unlike traditional climate programmes focused on policy or infrastructure, this initiative recognizes that effective climate health responses must be developed by those experiencing the impacts firsthand. The course enables health workers to share their own experiences, learn from colleagues facing similar challenges, and develop both individual and collective responses.
Dr. Eme Ngeda from the Democratic Republic of Congo captured this approach during the 2023 events: “We are all responsible for these climate disruptions. We must sensitize our populations in waste management and sensitize how to reform our healthcare providers to face resilience, face disasters.”
The programme connects leaders from more than 4,000 locally-led health organizations through TGLF’s REACH network, enabling them to become programme partners supporting their health workers in developing climate-health leadership skills.
Building global solutions by connecting local, indigenous knowledge and expertise
The inaugural course offers health professionals worldwide the opportunity to learn from documented experiences of colleagues who are facing unprecedented consequences of climate change on health. Rather than lectures or theoretical frameworks, the programme employs structured reflection and peer feedback cycles, enabling participants to develop actionable implementation plans informed by peer knowledge and global guidance.
The course covers four key areas based on health worker experiences:
- Climate and environmental changes: Recognizing connections between climate and health in local communities.
- Health impacts on communities: Understanding direct health impacts, food security, and mental health effects.
- Changing disease patterns: Managing infectious diseases, respiratory conditions, and healthcare access challenges.
- Community responses and adaptations: Implementing local solutions and innovations from peer experiences.
Participants earn verified certificates aligned to professional development competency frameworks. Upon completion, they join TGLF’s global community of health practitioners for ongoing peer support and collaboration.
The urgency of now
The programme launches at a critical moment. Climate change impacts on health are accelerating, particularly in low- and middle-income countries where health systems are least equipped to respond. Yet these same regions are producing innovative, resource-efficient solutions that could benefit communities worldwide.
As one health worker reflected during the 2023 events: “Although climate change is a global phenomenon, it is affecting very, very locally people in very different ways.” The new programme acknowledges this reality while creating pathways for local solutions to inform global responses.
The course is available in English and French, designed to work on mobile devices and basic internet connections. It is free for health workers in participating countries.
For health workers who have been managing climate impacts in isolation, the programme offers something unprecedented: the chance to learn from colleagues who truly understand their challenges and to contribute their own expertise to a growing global knowledge base.
As the climate health crisis deepens, the solutions may well come from those who have been living with its impacts longest—if we finally give them the platforms and recognition they deserve.
Image: The Geneva Learning Foundation Collection © 2025
#CertificatePeerLearningProgrammeForLeadershipInClimateChangeAndHealth #climate #climateChangeAndHealth #health #peerLearning #TheGenevaLearningFoundation
-
Climate change and health: a new peer learning programme by and for health workers from the most climate-vulnerable countries
GENEVA, Switzerland, 23 July 2025 (The Geneva Learning Foundation) –Today, The Geneva Learning Foundation (TGLF) announces the launch of “Learning to lead change on the frontline of climate change and health,” the inaugural course in a new certificate programme designed by and for professionals facing climate change impacts on health.
Enrollment is now open. The course will launch on 11 August 2025.
- English: https://go.learning.foundation/tglf/c/20132
- French: https://go.learning.foundation/tglf/c/21450
Two years ago today, nearly 5,000 health professionals from across the developing world gathered online for an unprecedented conversation. They shared something most climate scientists had never heard: detailed, firsthand accounts of how rising temperatures, extreme weather, and environmental changes were already devastating the health of their communities.
The stories were urgent and specific. A nurse in Ghana described managing surges of malaria after unprecedented flooding. A community health worker in Bangladesh explained how cholera outbreaks followed every major storm. A pharmacist in Nigeria watched children suffer malnutrition as crops failed during extended droughts.
“I can hear the worry in your voices,” one global health partner told participants during those historic July 2023 events, “and I really respect the time that you are giving to tell us about what is happening to you directly.”
Connecting the dots from individual impact to systemic crisis
While climate change dominates headlines for its environmental and economic impacts, a parallel health crisis has been quietly unfolding in clinics and hospitals across Africa, Asia, and Latin America. Health workers have become first-hand witnesses to climate change’s human toll.
Dr. Seydou Mohamed Ouedraogo from Burkina Faso described devastating floods that “really marked the memory of the inhabitants” and led to cascading health impacts.
Felix Kole from Gambia reported that “wells have turned to salty water” due to rising sea levels, while extreme heat meant “people are no longer sleeping inside their houses,” creating new security and health complications.
Rebecca Akello, a public health nurse from Uganda, documented malnutrition impacts directly: “During dry spells where there is no food, children come and their growth monitoring shows they really score low weight for age.”
Health professionals like Dr. Iktiyar Kandaker from Bangladesh already get that this is a systemic challenge: “Our health system is not prepared to actually address these situations. So this is a combined challenge… but it requires a lot of time to fix it.”
These health workers serve as what TGLF calls “trusted advisors”—over half describe themselves as being like “members of the family” to the populations they serve. Yet until now, they have had no structured way to learn from each other’s experiences or develop coordinated responses to climate health challenges.
Learning from those who know because they are there every day
“It is something that all of us have to join hands to be able to do the most we can to educate our communities on what they can do,” said Monica Agu, a community pharmacist from Nigeria who participated in the founding 2023 events. Her words captured the collaborative spirit that has driven the programme’s development.
The new certificate programme employs TGLF’s proven peer learning methodology, recognizing that health workers are already implementing life-saving climate adaptations with limited resources. During the 2023 events, participants shared examples of modified immunization schedules during heat waves, cholera outbreak management after flooding, and maintaining health services during extreme weather events.
“We believe that investing in health workers is one of the best ways to accelerate and strengthen the response to climate change impacts on health,” explains TGLF Executive Director Reda Sadki.
The programme has been developed from comprehensive analysis of health worker experiences documented since 2023. Most observations come from small and medium-sized communities in the most climate-vulnerable countries.
For health, a different kind of climate action
Unlike traditional climate programmes focused on policy or infrastructure, this initiative recognizes that effective climate health responses must be developed by those experiencing the impacts firsthand. The course enables health workers to share their own experiences, learn from colleagues facing similar challenges, and develop both individual and collective responses.
Dr. Eme Ngeda from the Democratic Republic of Congo captured this approach during the 2023 events: “We are all responsible for these climate disruptions. We must sensitize our populations in waste management and sensitize how to reform our healthcare providers to face resilience, face disasters.”
The programme connects leaders from more than 4,000 locally-led health organizations through TGLF’s REACH network, enabling them to become programme partners supporting their health workers in developing climate-health leadership skills.
Building global solutions by connecting local, indigenous knowledge and expertise
The inaugural course offers health professionals worldwide the opportunity to learn from documented experiences of colleagues who are facing unprecedented consequences of climate change on health. Rather than lectures or theoretical frameworks, the programme employs structured reflection and peer feedback cycles, enabling participants to develop actionable implementation plans informed by peer knowledge and global guidance.
The course covers four key areas based on health worker experiences:
- Climate and environmental changes: Recognizing connections between climate and health in local communities.
- Health impacts on communities: Understanding direct health impacts, food security, and mental health effects.
- Changing disease patterns: Managing infectious diseases, respiratory conditions, and healthcare access challenges.
- Community responses and adaptations: Implementing local solutions and innovations from peer experiences.
Participants earn verified certificates aligned to professional development competency frameworks. Upon completion, they join TGLF’s global community of health practitioners for ongoing peer support and collaboration.
The urgency of now
The programme launches at a critical moment. Climate change impacts on health are accelerating, particularly in low- and middle-income countries where health systems are least equipped to respond. Yet these same regions are producing innovative, resource-efficient solutions that could benefit communities worldwide.
As one health worker reflected during the 2023 events: “Although climate change is a global phenomenon, it is affecting very, very locally people in very different ways.” The new programme acknowledges this reality while creating pathways for local solutions to inform global responses.
The course is available in English and French, designed to work on mobile devices and basic internet connections. It is free for health workers in participating countries.
For health workers who have been managing climate impacts in isolation, the programme offers something unprecedented: the chance to learn from colleagues who truly understand their challenges and to contribute their own expertise to a growing global knowledge base.
As the climate health crisis deepens, the solutions may well come from those who have been living with its impacts longest—if we finally give them the platforms and recognition they deserve.
Image: The Geneva Learning Foundation Collection © 2025
#CertificatePeerLearningProgrammeForLeadershipInClimateChangeAndHealth #climate #climateChangeAndHealth #health #peerLearning #TheGenevaLearningFoundation
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What is The Geneva Learning Foundation’s Impact Accelerator?
Imagine a social worker in Ukraine supporting children affected by the humanitarian crisis. Thousands of kilometers away, a radiation specialist in Japan is trying to find effective ways to communicate with local communities. In Nigeria, a health worker is tackling how to increase immunization coverage in their remote village. These professionals face very different challenges in very different places. Yet when they joined their first “Impact Accelerator”, something remarkable happened. They all found a way forward. They all made real progress. They all discovered they are not alone.
The Impact Accelerator is a simple, practical method developed by The Geneva Learning Foundation that helps professionals turn intent into action, results, and outcomes. It has worked equally well in every country where it has been tried. It has helped people – whatever their knowledge domain or context – strengthen action and accelerate progress to improve health outcomes. Each time, in each place, whatever the challenge, it has produced the same powerful results.
The social worker joins other professionals facing similar challenges. The radiation specialist connects with safety experts dealing with comparable concerns. The health worker collaborates with others working to improve immunization. Each group shares a common purpose.
What makes the Impact Accelerator different?
Most training programs teach you something and then send you away. You return to your workplace full of ideas but face the same obstacles. You have new knowledge but struggle to apply it. (Some people call this “knowledge transfer” but it is not only about knowledge. Others call this the “applicability problem”.) You feel alone with your challenges.
The Impact Accelerator works differently. It stays with you as you implement change. It connects you with others facing similar challenges. It helps you take small, concrete steps each week toward your bigger goal.
Each Impact Accelerator brings together professionals working on the same type of challenge. Social workers who support children join with others who do the same – but the group may also include teachers and psychologists they do not usually work with. Safety specialists connect with safety specialists, but also people in other job roles. It is their shared purpose that makes this diversity productive: every discussion, every shared experience, every piece of advice directly applies to their work.
Think of it like learning to ride a bicycle. Traditional training is like someone explaining how bicycles work. The Impact Accelerator is like having someone run alongside you, keeping you steady as you pedal, cheering when you succeed, and helping you get back on when you fall. Everyone learns to ride, together. And everyone is going somewhere.
How does the Impact Accelerator work?
The Impact Accelerator follows a simple weekly rhythm that fits into daily work. It is learning-based work and work-based learning.
Monday: Set your goal
Every Monday, you decide on one specific action you will complete by Friday. Not a vague hope or a grand plan. One concrete thing you can actually do.
For example:
- “I will create a safe space activity for five children showing signs of trauma.”
- “I will develop a visual guide for the new radiation monitoring procedures.”
- “I will meet with three community leaders to discuss vaccine concerns.”
You share this goal with others in the Accelerator. This creates accountability. You know that on Friday, your peers will ask how it turned out.
Wednesday: Check in with peers
Midweek, you connect with others in your group who face the same type of challenges. You share what is working, what is difficult, and what you are learning.
This is where magic happens. Someone else tried something that failed. Now you know to try differently. Another person found a creative solution. Now you can adapt it for your situation. You realize you are part of something bigger than yourself.
Friday: Report and reflect
On Friday, you report on your progress. Did you achieve your goal? What happened when you tried? What did you learn?
This is not about judging success or failure. Sometimes the most valuable learning comes from things that did not work as expected. The important thing is that you took action, you reflected on what happened, and you are ready to try again next week.
Monday again: Build on what you learned
The next Monday, you set a new goal. But now you are not starting from zero. You have the experience from last week. You have ideas from your peers. You have momentum.
Week by week, action by action, you make progress toward your larger goal.
The power of structured support in the Impact Accelerator
The Impact Accelerator provides several types of support to help you succeed.
Peer learning networks
You join a community of professionals who understand your challenges because they face similar ones.
Each Impact Accelerator brings together people working on the same type of challenge. This shared purpose means that every suggestion, every idea, every lesson learned is likely to be relevant to your work. The learning comes not from distant experts but from people doing the same work you do. Their solutions are practical and tested in real conditions like yours.
Guided structure
While you choose your own goals and actions, the Accelerator provides a framework that keeps you moving forward. The weekly rhythm creates momentum. The reporting requirements ensure reflection. The peer connections prevent isolation.
This structure is like the banks of a river. The water (your energy and creativity) flows freely, but the banks keep it moving in a productive direction.
Expert guidance when needed
Sometimes you need specific technical input or help with a particular challenge. The Accelerator provides “guides on the side” – experts who offer targeted support without taking over your process. They help you think through problems and connect you with resources, but you remain in charge of your own change effort.
What participants achieve
Across different countries and different challenges, Impact Accelerator participants report similar outcomes.
Increased confidence
“Before, I knew what should be done but felt overwhelmed about how to start. Now I take one step at a time and see real progress.” This confidence comes from successfully completing weekly actions and seeing their impact.
Tangible progress
Participants do not just learn about change; they create it. A vaccination program reaches new communities. Safety procedures actually get implemented. Children receive support when they need it. The changes may start small, but they are real and they grow.
Expanded networks
“I used to feel like I was the only one facing these problems. Now I have colleagues across my country who understand and support me.” These networks last beyond the Accelerator, providing ongoing support and collaboration.
Enhanced problem-solving
Through weekly practice and peer exchange, participants develop stronger skills for analyzing challenges and developing solutions. They learn to break big problems into manageable actions and to adapt based on results.
Resilience in facing obstacles
Every change effort faces barriers. The Accelerator helps participants expect these obstacles and work through them with peer support rather than giving up when things get difficult.
How can the same methodology work everywhere?
The Impact Accelerator has succeeded across vastly different contexts – from supporting children in Ukrainian cities to enhancing radiation safety in Japanese facilities to improving immunization in Nigerian villages. Each Accelerator focuses on one specific challenge area, bringing together professionals who share that common purpose. Why does the same approach work for such different challenges?
The answer lies in focusing on universal elements of successful change:
- Breaking big goals into weekly actions;
- Learning from peers who understand your specific context and challenges;
- Reflecting on what works and what does not;
- Building momentum through consistent progress; and
- Creating accountability through a community united by shared purpose.
Each group focuses on their specific challenge and context, but the process of creating change remains remarkably similar.
A typical participant journey in the Impact Accelerator
Let us follow Yuliia, a social worker in Ukraine helping children affected by the humanitarian crisis.
Week 1: Getting started
Yuliia joins the Impact Accelerator after developing her action plan. Her big goal: establish effective psychological support for 50 displaced children in her community center within three months.
On Monday, she sets her first weekly goal: “During daily activities, I will observe and document how 10 children are affected.”
By Friday, she has detailed observations. She notices that loud noises sometimes cause reactions in most children, and several withdraw completely during group activities. This gives her concrete starting points.
Week 2: Building on learning
Based on her observations, Yuliia sets a new goal: “I will create a quiet corner with calming materials and test it with three children who are withdrawn.”
During the Wednesday check-in, another social worker shares how she uses art therapy for non-verbal expression with traumatized children. A colleague working in a different city describes success with sensory materials. Yuliia incorporates both ideas into her quiet corner.
The quiet corner proves successful – two of the three children spend time there and begin to engage with the materials. One child draws for the first time since arriving at the center.
Week 3: Creative solutions
Yuliia’s new goal: “I will develop a simple ‘feelings chart’ with visual cues and introduce it during morning circle time.”
Her peers from Ukraine and all over Europe – all working with children – help refine the idea. A psychologist from another region shares that abstract emotions are hard for traumatized children to identify. She suggests using colors and weather symbols instead of facial expressions. Another colleague recommends making the chart interactive rather than static.
The feelings chart becomes a breakthrough tool. Children who never spoke about their emotions begin pointing to images. Yuliia’s colleagues can better understand and respond to children’s needs.
Week 4: Scaling what works
Energized by success, Yuliia aims higher: “I will train two other staff members to use the quiet corner and feelings chart, and create a simple guide for these tools.”
By now, Yuliia has concrete evidence that these approaches work. She documents specific examples of children’s progress. Her guide is so practical that the center director wants to share it with other locations.
The ripple effect
Yuliia’s tools spread throughout the network of centers supporting displaced children. Through the Accelerator network, colleagues adapt her approaches for different age groups and settings. Soon, hundreds of children across Ukraine benefit from these simple but effective interventions.
The evidence of impact
The true test of any approach is whether it creates lasting change. Impact Accelerator participants consistently report:
- Specific improvements in their work that they can measure and document;
- Sustained changes that continue after the Accelerator ends;
- Solutions that others adopt and spread;
- Professional growth that enhances all their future work; and
- Networks that provide ongoing support and learning.
These outcomes appear whether participants work on mental health support in Ukraine, radiation safety in Japan, or immunization in Nigeria. The challenges differ, but the pattern of success remains consistent.
How we prove the Accelerator makes a difference
In global health, the biggest challenge is proving that your intervention actually caused the improvements you see. This is called “attribution.” How do we know that better health outcomes happened because of the Impact Accelerator and not for other reasons?
The Geneva Learning Foundation solves this challenge through a three-step process that connects the dots between learning, action, and results.
Step 1: Measuring where we start
Before participants begin taking action, they document their baseline – the current situation they want to improve. For example:
- A social worker records how many children show severe trauma symptoms.
- A radiation specialist documents current safety incident rates.
- A health worker notes the vaccination coverage in their area.
These starting numbers give us a clear picture of where improvement begins.
Step 2: Tracking progress and actions
Every week, participants complete “acceleration reports” that capture two things:
- The specific actions they took; and
- Any changes they observe in their measurements.
This creates a detailed record connecting what participants do to what happens as a result. Week by week, the picture becomes clearer.
Step 3: Proving the connection
Here is where the Impact Accelerator becomes special. When participants see improvements, they must answer a crucial question: “How much of this change happened because of what you learned and did through the Accelerator?”
But they cannot just claim credit. They must prove it to their peers by showing:
- Exactly which actions led to which results;
- Why the changes would not have happened without their intervention; and
- Evidence that their specific approach made the difference.
This peer review process is powerful. Your colleagues understand your context. They know what is realistic. They can spot when claims are too bold or when someone is being too modest. They ask tough questions that help clarify what really caused the improvements.
After the first-ever Accelerator in 2019, we compared the implementation progress after six months between those who joined this final stage and a control group that also developed action plans, but did not join.
Why this method works
This approach solves several problems that make attribution difficult:
- Traditional studies often cannot capture the complexity of real-world change. The Impact Accelerator’s method shows not just that change happened, but how and why it happened.
- Self-reporting can be unreliable when people work alone. But when you must convince peers who understand your work, the reports become more accurate and honest.
- Numbers alone do not tell the whole story. By combining measurements with detailed descriptions of actions and peer validation, we get a complete picture of how change happens.
The invitation to act
Around the world, professionals like you are transforming their work through the Impact Accelerator. They start with the same doubts you might have: “Can I really create change? Will this work in my context? Do I have time for this?”
Week by week, action by action, they discover the answer is yes. Yes, they can create change. Yes, it works in their context. Yes, they can find time because the Accelerator fits into their real work rather than adding to it.
The Impact Accelerator does not promise overnight transformation. It offers something better: a proven process for creating real, sustainable change through your own efforts, supported by peers who understand your journey.
If you work in a field where you seek to make a difference, the Impact Accelerator can help you move from good intentions to meaningful impact. The same process can work for you.
The question is not whether the Impact Accelerator can help you create change. The question is: What change do you want to create?
Your journey can begin Monday.
Image: The Geneva Learning Foundation Collection © 2025
References
Sadki, R., 2022. Learning for Knowledge Creation: The WHO Scholar Program. https://doi.org/10.59350/j4ptf-x6x22
Umbelino-Walker, I., Szylovec, A.P., Dakam, B.A., Monglo, A., Jones, I., Mbuh, C., Sadki, R., Brooks, A., 2024. Towards a sustainable model for a digital learning network in support of the Immunization Agenda 2030 –a mixed methods study with a transdisciplinary component. PLOS Global Public Health 4, e0003855. https://doi.org/10.1371/journal.pgph.0003855
Watkins, K.E., Sadki, R., Kim, K., Suh, B., 2019. Changing Learning Paradigms in a Global Health Agency, in: Evidence-Based Initiatives for Organizational Change and Development. IGI Global, pp. 693–703. https://doi.org/10.4018/978-1-5225-6155-2.ch050
Watkins, K.E., Sandmann, L.R., Dailey, C.A., Li, B., Yang, S.-E., Galen, R.S., Sadki, R., 2022. Accelerating problem-solving capacities of sub-national public health professionals: an evaluation of a digital immunization training intervention. BMC Health Services Research 22. https://doi.org/10.1186/s12913-022-08138-4
#attribution #continuousLearning #healthOutcomes #impactAccelerator #leadership #learnByDoing #newLearning #peerLearning #genevaLearningFoundation
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What is The Geneva Learning Foundation’s Impact Accelerator?
Imagine a social worker in Ukraine supporting children affected by the humanitarian crisis. Thousands of kilometers away, a radiation specialist in Japan is trying to find effective ways to communicate with local communities. In Nigeria, a health worker is tackling how to increase immunization coverage in their remote village. These professionals face very different challenges in very different places. Yet when they joined their first “Impact Accelerator”, something remarkable happened. They all found a way forward. They all made real progress. They all discovered they are not alone.
The Impact Accelerator is a simple, practical method developed by The Geneva Learning Foundation that helps professionals turn intent into action, results, and outcomes. It has worked equally well in every country where it has been tried. It has helped people – whatever their knowledge domain or context – strengthen action and accelerate progress to improve health outcomes. Each time, in each place, whatever the challenge, it has produced the same powerful results.
The social worker joins other professionals facing similar challenges. The radiation specialist connects with safety experts dealing with comparable concerns. The health worker collaborates with others working to improve immunization. Each group shares a common purpose.
What makes the Impact Accelerator different?
Most training programs teach you something and then send you away. You return to your workplace full of ideas but face the same obstacles. You have new knowledge but struggle to apply it. (Some people call this “knowledge transfer” but it is not only about knowledge. Others call this the “applicability problem”.) You feel alone with your challenges.
The Impact Accelerator works differently. It stays with you as you implement change. It connects you with others facing similar challenges. It helps you take small, concrete steps each week toward your bigger goal.
Each Impact Accelerator brings together professionals working on the same type of challenge. Social workers who support children join with others who do the same – but the group may also include teachers and psychologists they do not usually work with. Safety specialists connect with safety specialists, but also people in other job roles. It is their shared purpose that makes this diversity productive: every discussion, every shared experience, every piece of advice directly applies to their work.
Think of it like learning to ride a bicycle. Traditional training is like someone explaining how bicycles work. The Impact Accelerator is like having someone run alongside you, keeping you steady as you pedal, cheering when you succeed, and helping you get back on when you fall. Everyone learns to ride, together. And everyone is going somewhere.
How does the Impact Accelerator work?
The Impact Accelerator follows a simple weekly rhythm that fits into daily work. It is learning-based work and work-based learning.
Monday: Set your goal
Every Monday, you decide on one specific action you will complete by Friday. Not a vague hope or a grand plan. One concrete thing you can actually do.
For example:
- “I will create a safe space activity for five children showing signs of trauma.”
- “I will develop a visual guide for the new radiation monitoring procedures.”
- “I will meet with three community leaders to discuss vaccine concerns.”
You share this goal with others in the Accelerator. This creates accountability. You know that on Friday, your peers will ask how it turned out.
Wednesday: Check in with peers
Midweek, you connect with others in your group who face the same type of challenges. You share what is working, what is difficult, and what you are learning.
This is where magic happens. Someone else tried something that failed. Now you know to try differently. Another person found a creative solution. Now you can adapt it for your situation. You realize you are part of something bigger than yourself.
Friday: Report and reflect
On Friday, you report on your progress. Did you achieve your goal? What happened when you tried? What did you learn?
This is not about judging success or failure. Sometimes the most valuable learning comes from things that did not work as expected. The important thing is that you took action, you reflected on what happened, and you are ready to try again next week.
Monday again: Build on what you learned
The next Monday, you set a new goal. But now you are not starting from zero. You have the experience from last week. You have ideas from your peers. You have momentum.
Week by week, action by action, you make progress toward your larger goal.
The power of structured support in the Impact Accelerator
The Impact Accelerator provides several types of support to help you succeed.
Peer learning networks
You join a community of professionals who understand your challenges because they face similar ones.
Each Impact Accelerator brings together people working on the same type of challenge. This shared purpose means that every suggestion, every idea, every lesson learned is likely to be relevant to your work. The learning comes not from distant experts but from people doing the same work you do. Their solutions are practical and tested in real conditions like yours.
Guided structure
While you choose your own goals and actions, the Accelerator provides a framework that keeps you moving forward. The weekly rhythm creates momentum. The reporting requirements ensure reflection. The peer connections prevent isolation.
This structure is like the banks of a river. The water (your energy and creativity) flows freely, but the banks keep it moving in a productive direction.
Expert guidance when needed
Sometimes you need specific technical input or help with a particular challenge. The Accelerator provides “guides on the side” – experts who offer targeted support without taking over your process. They help you think through problems and connect you with resources, but you remain in charge of your own change effort.
What participants achieve
Across different countries and different challenges, Impact Accelerator participants report similar outcomes.
Increased confidence
“Before, I knew what should be done but felt overwhelmed about how to start. Now I take one step at a time and see real progress.” This confidence comes from successfully completing weekly actions and seeing their impact.
Tangible progress
Participants do not just learn about change; they create it. A vaccination program reaches new communities. Safety procedures actually get implemented. Children receive support when they need it. The changes may start small, but they are real and they grow.
Expanded networks
“I used to feel like I was the only one facing these problems. Now I have colleagues across my country who understand and support me.” These networks last beyond the Accelerator, providing ongoing support and collaboration.
Enhanced problem-solving
Through weekly practice and peer exchange, participants develop stronger skills for analyzing challenges and developing solutions. They learn to break big problems into manageable actions and to adapt based on results.
Resilience in facing obstacles
Every change effort faces barriers. The Accelerator helps participants expect these obstacles and work through them with peer support rather than giving up when things get difficult.
How can the same methodology work everywhere?
The Impact Accelerator has succeeded across vastly different contexts – from supporting children in Ukrainian cities to enhancing radiation safety in Japanese facilities to improving immunization in Nigerian villages. Each Accelerator focuses on one specific challenge area, bringing together professionals who share that common purpose. Why does the same approach work for such different challenges?
The answer lies in focusing on universal elements of successful change:
- Breaking big goals into weekly actions;
- Learning from peers who understand your specific context and challenges;
- Reflecting on what works and what does not;
- Building momentum through consistent progress; and
- Creating accountability through a community united by shared purpose.
Each group focuses on their specific challenge and context, but the process of creating change remains remarkably similar.
A typical participant journey in the Impact Accelerator
Let us follow Yuliia, a social worker in Ukraine helping children affected by the humanitarian crisis.
Week 1: Getting started
Yuliia joins the Impact Accelerator after developing her action plan. Her big goal: establish effective psychological support for 50 displaced children in her community center within three months.
On Monday, she sets her first weekly goal: “During daily activities, I will observe and document how 10 children are affected.”
By Friday, she has detailed observations. She notices that loud noises sometimes cause reactions in most children, and several withdraw completely during group activities. This gives her concrete starting points.
Week 2: Building on learning
Based on her observations, Yuliia sets a new goal: “I will create a quiet corner with calming materials and test it with three children who are withdrawn.”
During the Wednesday check-in, another social worker shares how she uses art therapy for non-verbal expression with traumatized children. A colleague working in a different city describes success with sensory materials. Yuliia incorporates both ideas into her quiet corner.
The quiet corner proves successful – two of the three children spend time there and begin to engage with the materials. One child draws for the first time since arriving at the center.
Week 3: Creative solutions
Yuliia’s new goal: “I will develop a simple ‘feelings chart’ with visual cues and introduce it during morning circle time.”
Her peers from Ukraine and all over Europe – all working with children – help refine the idea. A psychologist from another region shares that abstract emotions are hard for traumatized children to identify. She suggests using colors and weather symbols instead of facial expressions. Another colleague recommends making the chart interactive rather than static.
The feelings chart becomes a breakthrough tool. Children who never spoke about their emotions begin pointing to images. Yuliia’s colleagues can better understand and respond to children’s needs.
Week 4: Scaling what works
Energized by success, Yuliia aims higher: “I will train two other staff members to use the quiet corner and feelings chart, and create a simple guide for these tools.”
By now, Yuliia has concrete evidence that these approaches work. She documents specific examples of children’s progress. Her guide is so practical that the center director wants to share it with other locations.
The ripple effect
Yuliia’s tools spread throughout the network of centers supporting displaced children. Through the Accelerator network, colleagues adapt her approaches for different age groups and settings. Soon, hundreds of children across Ukraine benefit from these simple but effective interventions.
The evidence of impact
The true test of any approach is whether it creates lasting change. Impact Accelerator participants consistently report:
- Specific improvements in their work that they can measure and document;
- Sustained changes that continue after the Accelerator ends;
- Solutions that others adopt and spread;
- Professional growth that enhances all their future work; and
- Networks that provide ongoing support and learning.
These outcomes appear whether participants work on mental health support in Ukraine, radiation safety in Japan, or immunization in Nigeria. The challenges differ, but the pattern of success remains consistent.
How we prove the Accelerator makes a difference
In global health, the biggest challenge is proving that your intervention actually caused the improvements you see. This is called “attribution.” How do we know that better health outcomes happened because of the Impact Accelerator and not for other reasons?
The Geneva Learning Foundation solves this challenge through a three-step process that connects the dots between learning, action, and results.
Step 1: Measuring where we start
Before participants begin taking action, they document their baseline – the current situation they want to improve. For example:
- A social worker records how many children show severe trauma symptoms.
- A radiation specialist documents current safety incident rates.
- A health worker notes the vaccination coverage in their area.
These starting numbers give us a clear picture of where improvement begins.
Step 2: Tracking progress and actions
Every week, participants complete “acceleration reports” that capture two things:
- The specific actions they took; and
- Any changes they observe in their measurements.
This creates a detailed record connecting what participants do to what happens as a result. Week by week, the picture becomes clearer.
Step 3: Proving the connection
Here is where the Impact Accelerator becomes special. When participants see improvements, they must answer a crucial question: “How much of this change happened because of what you learned and did through the Accelerator?”
But they cannot just claim credit. They must prove it to their peers by showing:
- Exactly which actions led to which results;
- Why the changes would not have happened without their intervention; and
- Evidence that their specific approach made the difference.
This peer review process is powerful. Your colleagues understand your context. They know what is realistic. They can spot when claims are too bold or when someone is being too modest. They ask tough questions that help clarify what really caused the improvements.
After the first-ever Accelerator in 2019, we compared the implementation progress after six months between those who joined this final stage and a control group that also developed action plans, but did not join.
Why this method works
This approach solves several problems that make attribution difficult:
- Traditional studies often cannot capture the complexity of real-world change. The Impact Accelerator’s method shows not just that change happened, but how and why it happened.
- Self-reporting can be unreliable when people work alone. But when you must convince peers who understand your work, the reports become more accurate and honest.
- Numbers alone do not tell the whole story. By combining measurements with detailed descriptions of actions and peer validation, we get a complete picture of how change happens.
The invitation to act
Around the world, professionals like you are transforming their work through the Impact Accelerator. They start with the same doubts you might have: “Can I really create change? Will this work in my context? Do I have time for this?”
Week by week, action by action, they discover the answer is yes. Yes, they can create change. Yes, it works in their context. Yes, they can find time because the Accelerator fits into their real work rather than adding to it.
The Impact Accelerator does not promise overnight transformation. It offers something better: a proven process for creating real, sustainable change through your own efforts, supported by peers who understand your journey.
If you work in a field where you seek to make a difference, the Impact Accelerator can help you move from good intentions to meaningful impact. The same process can work for you.
The question is not whether the Impact Accelerator can help you create change. The question is: What change do you want to create?
Your journey can begin Monday.
Image: The Geneva Learning Foundation Collection © 2025
References
Sadki, R., 2022. Learning for Knowledge Creation: The WHO Scholar Program. https://doi.org/10.59350/j4ptf-x6x22
Umbelino-Walker, I., Szylovec, A.P., Dakam, B.A., Monglo, A., Jones, I., Mbuh, C., Sadki, R., Brooks, A., 2024. Towards a sustainable model for a digital learning network in support of the Immunization Agenda 2030 –a mixed methods study with a transdisciplinary component. PLOS Global Public Health 4, e0003855. https://doi.org/10.1371/journal.pgph.0003855
Watkins, K.E., Sadki, R., Kim, K., Suh, B., 2019. Changing Learning Paradigms in a Global Health Agency, in: Evidence-Based Initiatives for Organizational Change and Development. IGI Global, pp. 693–703. https://doi.org/10.4018/978-1-5225-6155-2.ch050
Watkins, K.E., Sandmann, L.R., Dailey, C.A., Li, B., Yang, S.-E., Galen, R.S., Sadki, R., 2022. Accelerating problem-solving capacities of sub-national public health professionals: an evaluation of a digital immunization training intervention. BMC Health Services Research 22. https://doi.org/10.1186/s12913-022-08138-4
#attribution #continuousLearning #healthOutcomes #impactAccelerator #leadership #learnByDoing #newLearning #peerLearning #genevaLearningFoundation
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Why peer learning is critical to survive the Age of Artificial Intelligence
María, a pediatrician in Argentina, works with an AI diagnostic system that can identify rare diseases, suggest treatment protocols, and draft reports in perfect medical Spanish. But something crucial is missing. The AI provides brilliant medical insights, yet María struggles to translate them into action in her community. What is needed to realize the promise of the Age of Artificial Intelligence?
Then she discovers the missing piece. Through a peer learning network—where health workers develop projects addressing real challenges, review each other’s work, and engage in facilitated dialogue—she connects with other health professionals across Latin America who are learning to work with AI as a collaborative partner. Together, they discover that AI becomes far more useful when combined with their understanding of local contexts, cultural practices, and community dynamics.
This speculative scenario, based on current AI developments and existing peer learning successes, illuminates a crucial insight as we ascend into the age of artificial intelligence. Eric Schmidt’s San Francisco Consensus predicts that within three to six years, AI will reason at expert levels, coordinate complex tasks through digital agents, and understand any request in natural language.
Understanding how peer learning can bridge AI capabilities and human thinking and action is critical to prepare for this future.
Collaboration in the Age of Artificial Intelligence
The three AI revolutions (language interfaces, reasoning systems, and agentic coordination) will offer unprecedented capabilities. If access is equitable, this will be available to any health worker, anywhere. Yet having access to these tools is just the beginning. The transformation will require humans to learn together how to collaborate effectively with AI.
Consider what becomes possible when health workers combine AI capabilities with collective human insight:
- AI analyzes disease patterns; peer networks share which interventions work in specific cultural contexts.
- AI suggests optimal treatment protocols; practitioners adapt them based on local resource availability.
- AI identifies at-risk populations; community workers know how to reach them effectively.
The magic happens in integration of AI and human capabiltiies through peer learning. Think of it this way: AI can analyze millions of health records to identify disease patterns, but it may not know that in your district, people avoid the Tuesday clinic because that is market day, or that certain communities trust traditional healers more than government health workers.
When epidemiologists share these contextual insights with peers facing similar challenges – through structured discussions and collaborative problem-solving – they learn together how to adapt AI’s analytical power to local realities.
For example, when an AI system identifies a disease cluster, epidemiologists in a peer network can share strategies for investigating it: one colleague might explain how they gained community trust for contact tracing, another might share how they adapted AI-generated survey questions to be culturally appropriate, and a third might demonstrate how they used AI predictions alongside traditional knowledge to improve outbreak response.
This collective learning where professionals teach each other how to blend AI’s computational abilities with human understanding of communities creates solutions more effective than either AI or individual expertise could achieve alone.
Understanding peer learning in the Age of Artificial Intelligence
Peer learning is not about professionals sharing anecdotes. It is a structured learning process where:
- Participants develop concrete projects addressing real challenges in their contexts, such as improving vaccination coverage or adapting AI tools for local use.
- Peers review each other’s work using expert-designed rubrics that ensure quality while encouraging innovation.
- Facilitated dialogue sessions help surface patterns across different contexts and generate collective insights.
- Continuous cycles of action, reflection, and revision transform individual experiences into shared wisdom.
- Every participant becomes both teacher and learner, contributing their unique insights while learning from others.
This approach differs fundamentally from traditional training because knowledge flows horizontally between peers rather than vertically from experts. When applied to human-AI collaboration, it enables rapid collective learning about what works, what fails, and why.
Why peer networks unlock the potential of the Age of Artificial Intelligence
Contextual intelligence through collective wisdom
AI systems train on global data and identify universal patterns. This is their strength. Human practitioners understand local contexts intimately. This is theirs. Peer learning networks create bridges between these complementary intelligences.
When a health worker discovers how to adapt AI-generated nutrition plans for local food availability, that insight becomes valuable to peers in similar contexts worldwide. Through structured sharing and review processes, the network creates a living library of contextual adaptations that make AI recommendations actionable.
Trust-building in the age of AI
Communities often view new technologies with suspicion. The most sophisticated AI cannot overcome this alone. But when local health workers learn from peers how to introduce AI as a helpful tool rather than a threatening replacement, acceptance grows.
In peer networks, practitioners share not just technical knowledge but communication strategies through structured dialogue: how to explain AI recommendations to skeptical patients, how to involve community leaders in AI-assisted health programs, how to maintain the human touch while using digital tools. This collective learning makes AI acceptable and valuable to communities that might otherwise reject it.
Distributed problem-solving
When AI provides a diagnosis or recommendation that seems inappropriate for local conditions, isolated practitioners might simply ignore it. But in peer networks with structured review processes, they can explore why the discrepancy exists and how to bridge it.
A teacher receives AI-generated lesson plans that assume resources her school lacks. Through her network’s collaborative problem-solving process, she finds teachers in similar situations who have created innovative adaptations. Together, they develop approaches that preserve AI’s pedagogical insights while working within real constraints.
The new architecture of collaborative learning
Working effectively with AI requires new forms of human collaboration built on three essential elements:
Reciprocal knowledge flows
When everyone has access to AI expertise, the most valuable learning happens between peers who share similar contexts and challenges. They teach each other not what AI knows, but how to make AI knowledge useful in their specific situations through:
- Structured project development and peer review;
- Regular assemblies where practitioners share experiences;
- Documentation of successful adaptations and failures;
- Continuous refinement based on collective feedback.
Structured experimentation
Peer networks provide safe spaces to experiment with AI collaboration. Through structured cycles of action and reflection, practitioners:
- Try AI recommendations in controlled ways;
- Document what works and what needs adaptation using shared frameworks;
- Share failures as valuable learning opportunities through facilitated sessions;
- Build collective knowledge about human-AI collaboration.
Continuous capability building
As AI capabilities evolve rapidly, no individual can keep pace alone. Peer networks create continuous learning environments where:
- Early adopters share new AI features through structured presentations;
- Groups explore emerging capabilities together in hands-on sessions;
- Collective intelligence about AI use grows through documented experiences;
- Everyone stays current through shared discovery and regular dialogue.
Evidence-based speculation: imagining peer networks that include both machines and humans
While the following examples are speculative, they build on current evidence from existing peer learning networks and emerging AI capabilities to imagine near-future possibilities.
The Nigerian immunization scenario
Based on Nigeria’s successful peer learning initiatives and current AI development trajectories, we can envision how AI-assisted immunization programs might work. AI could help identify optimal vaccine distribution patterns and predict which communities are at risk. Success would come when health workers form peer networks to share:
- Techniques for presenting AI predictions to community leaders effectively;
- Methods for adapting AI-suggested schedules to local market days and religious observances;
- Strategies for using AI insights while maintaining personal relationships that drive vaccine acceptance.
This scenario extrapolates from current successes in peer learning for immunization in Nigeria to imagine enhanced outcomes with AI partnership.
Climate health innovation networks
Drawing from existing climate health responses and AI’s growing environmental analysis capabilities, we can project how peer networks might function. As climate change creates unprecedented health challenges, AI models will predict impacts and suggest interventions. Community-based health workers could connect these ‘big data’ insights with their own local observations and experience to take action, sharing innovations like:
- Using AI climate predictions to prepare communities for heat waves;
- Adapting AI-suggested cooling strategies to local housing conditions;
- Combining traditional knowledge with AI insights for water management.
These possibilities build on documented peer learning successes in sharing health workers observations and insights about the impacts of climate change on the health of local communities.
Addressing AI’s limitations through collective wisdom
While AI offers powerful capabilities, we must acknowledge that technology is not neutral—AI systems carry biases from their training data, reflect the perspectives of their creators, and can perpetuate or amplify existing inequalities. Peer learning networks provide a crucial mechanism for identifying and addressing these limitations collectively.
Through structured dialogue and shared experiences, practitioners can:
- Document when AI recommendations reflect biases inappropriate for their contexts;
- Develop collective strategies for identifying and correcting AI biases;
- Share techniques for adapting AI outputs to ensure equity;
- Build shared understanding of AI’s limitations and appropriate use cases.
This collective vigilance and adaptation becomes essential for ensuring AI serves all communities fairly.
What this means for different stakeholders
For funders: Investing in collaborative capacity
The highest return on AI investment comes not from technology alone but from building human capacity to use it effectively. Peer learning networks:
- Multiply the impact of AI tools through shared adaptation strategies;
- Create sustainable capacity that grows with technological advancement;
- Generate innovations that improve AI applications for specific contexts;
- Build resilience through distributed expertise.
For practitioners: New collaborative competencies
Working effectively with AI requires skills best developed through structured peer learning:
- Partnership mindset: Seeing AI as a collaborative tool requiring human judgment.
- Adaptive expertise: Learning to blend AI capabilities with contextual knowledge.
- Reflective practice: Regularly examining what works in human-AI collaboration through structured reflection.
- Knowledge sharing: Contributing insights through peer review and dialogue that help others work better with AI.
For policymakers: Enabling collaborative ecosystems
Policies should support human-AI collaboration by:
- Funding peer learning infrastructure alongside AI deployment;
- Creating time and space for structured peer learning activities;
- Recognizing peer learning as essential professional development;
- Supporting documentation and spread of effective practices.
AI-human transformation through collaboration: A comparative view
Working with AI individuallyWorking with AI through structured peer networksPowerful tools but limited adaptation
Insights remain isolated
Success depends on individual skillContinuous adaptation through structured sharing
Insights multiply across network through peer review
Collective wisdom enhances individual capabilityAI recommendations may miss local context
Trial and error in isolation
Slow spread of effective practicesContext-aware applications emerge through dialogue
Structured experimentation with collective learning
Rapid diffusion through documented innovationsOverwhelmed by rapid AI changes
Struggling to keep pace alone
Uncertainty about appropriate useCollective sense-making through facilitated sessions
Shared discovery in peer projects
Growing confidence through structured supportThe collaborative future
As AI capabilities expand, two paths emerge:
Path 1: Individuals struggle alone to make sense of AI tools, leading to uneven adoption, missed opportunities, and growing inequality between those who figure it out and those who do not.
Path 2: Structured peer networks enable collective learning about human-AI collaboration, leading to widespread effective use, continuous innovation, and shared benefit from AI advances.
What determines outcomes is how humans organize to learn and work together with AI through structured peer learning processes.
María’s projected transformation
Six months after her initial struggles, we can envision how María’s experience might transform. Through structured peer learning—project development, peer review, and facilitated dialogue—she could learn to see AI not as a foreign expert imposing solutions, but as a knowledgeable colleague whose insights she can adapt and apply.
Based on current peer learning practices, she might discover techniques from colleagues across Latin America and the rest of the world:
- Methods for using AI diagnosis as a conversation starter with traditional healers;
- Strategies for validating AI recommendations through community health committees;
- Approaches for using AI analytics to support (not replace) community knowledge.
Following the pattern of peer learning networks, Maríawould begin contributing her own innovations through structured sharing, particularly around integrating AI insights with indigenous healing practices. Her documented approaches would spread through peer review and dialogue, helping thousands of health workers make AI truly useful in their communities.
Conclusion: The multiplication effect
AI transformation promises to augment human capabilities dramatically. Language interfaces will democratize access to advanced tools. Reasoning systems will provide expert-level analysis. Agentic AI will coordinate complex operations. These capabilities are beginning to transform what individuals can accomplish.
But the true multiplication effect will come through structured peer learning networks. When thousands of practitioners share how to work effectively with AI through systematic project work, peer review, and facilitated dialogue, they create collective intelligence about human-AI collaboration that no individual could develop alone. They transform AI from an impressive but alien technology into a natural extension of human capability.
For funders, this means the highest-impact investments combine AI tools with structured peer learning infrastructure. For policymakers, it means creating conditions where collaborative learning flourishes alongside technological deployment. For practitioners, it means embracing both AI partnership and peer collaboration through structured processes as essential to professional practice.
The future of human progress may rest on our ability to find effective ways to build powerful collaboration in networks that combine human and artificial intelligence. When we learn together through structured peer learning how to work with AI, we multiply not just individual capability but collective capacity to address the complex challenges facing our world.
AI is still emergent, changing constantly and rapidly. The peer learning methods are proven: we know a lot about how humans learn and collaborate. The question is how quickly we can scale this collaborative approach to match the pace of AI advancement. In that race, structured peer learning is not optional—it is essential.
Image: The Geneva Learning Foundation Collection © 2025
#ArtificialIntelligence #climateAndHealth #globalHealth #peerLearning #SanFranciscoConsensus -
Why peer learning is critical to survive the Age of Artificial Intelligence
María, a pediatrician in Argentina, works with an AI diagnostic system that can identify rare diseases, suggest treatment protocols, and draft reports in perfect medical Spanish. But something crucial is missing. The AI provides brilliant medical insights, yet María struggles to translate them into action in her community. What is needed to realize the promise of the Age of Artificial Intelligence?
Then she discovers the missing piece. Through a peer learning network—where health workers develop projects addressing real challenges, review each other’s work, and engage in facilitated dialogue—she connects with other health professionals across Latin America who are learning to work with AI as a collaborative partner. Together, they discover that AI becomes far more useful when combined with their understanding of local contexts, cultural practices, and community dynamics.
This speculative scenario, based on current AI developments and existing peer learning successes, illuminates a crucial insight as we ascend into the age of artificial intelligence. Eric Schmidt’s San Francisco Consensus predicts that within three to six years, AI will reason at expert levels, coordinate complex tasks through digital agents, and understand any request in natural language.
Understanding how peer learning can bridge AI capabilities and human thinking and action is critical to prepare for this future.
Collaboration in the Age of Artificial Intelligence
The three AI revolutions (language interfaces, reasoning systems, and agentic coordination) will offer unprecedented capabilities. If access is equitable, this will be available to any health worker, anywhere. Yet having access to these tools is just the beginning. The transformation will require humans to learn together how to collaborate effectively with AI.
Consider what becomes possible when health workers combine AI capabilities with collective human insight:
- AI analyzes disease patterns; peer networks share which interventions work in specific cultural contexts.
- AI suggests optimal treatment protocols; practitioners adapt them based on local resource availability.
- AI identifies at-risk populations; community workers know how to reach them effectively.
The magic happens in integration of AI and human capabiltiies through peer learning. Think of it this way: AI can analyze millions of health records to identify disease patterns, but it may not know that in your district, people avoid the Tuesday clinic because that is market day, or that certain communities trust traditional healers more than government health workers.
When epidemiologists share these contextual insights with peers facing similar challenges – through structured discussions and collaborative problem-solving – they learn together how to adapt AI’s analytical power to local realities.
For example, when an AI system identifies a disease cluster, epidemiologists in a peer network can share strategies for investigating it: one colleague might explain how they gained community trust for contact tracing, another might share how they adapted AI-generated survey questions to be culturally appropriate, and a third might demonstrate how they used AI predictions alongside traditional knowledge to improve outbreak response.
This collective learning where professionals teach each other how to blend AI’s computational abilities with human understanding of communities creates solutions more effective than either AI or individual expertise could achieve alone.
Understanding peer learning in the Age of Artificial Intelligence
Peer learning is not about professionals sharing anecdotes. It is a structured learning process where:
- Participants develop concrete projects addressing real challenges in their contexts, such as improving vaccination coverage or adapting AI tools for local use.
- Peers review each other’s work using expert-designed rubrics that ensure quality while encouraging innovation.
- Facilitated dialogue sessions help surface patterns across different contexts and generate collective insights.
- Continuous cycles of action, reflection, and revision transform individual experiences into shared wisdom.
- Every participant becomes both teacher and learner, contributing their unique insights while learning from others.
This approach differs fundamentally from traditional training because knowledge flows horizontally between peers rather than vertically from experts. When applied to human-AI collaboration, it enables rapid collective learning about what works, what fails, and why.
Why peer networks unlock the potential of the Age of Artificial Intelligence
Contextual intelligence through collective wisdom
AI systems train on global data and identify universal patterns. This is their strength. Human practitioners understand local contexts intimately. This is theirs. Peer learning networks create bridges between these complementary intelligences.
When a health worker discovers how to adapt AI-generated nutrition plans for local food availability, that insight becomes valuable to peers in similar contexts worldwide. Through structured sharing and review processes, the network creates a living library of contextual adaptations that make AI recommendations actionable.
Trust-building in the age of AI
Communities often view new technologies with suspicion. The most sophisticated AI cannot overcome this alone. But when local health workers learn from peers how to introduce AI as a helpful tool rather than a threatening replacement, acceptance grows.
In peer networks, practitioners share not just technical knowledge but communication strategies through structured dialogue: how to explain AI recommendations to skeptical patients, how to involve community leaders in AI-assisted health programs, how to maintain the human touch while using digital tools. This collective learning makes AI acceptable and valuable to communities that might otherwise reject it.
Distributed problem-solving
When AI provides a diagnosis or recommendation that seems inappropriate for local conditions, isolated practitioners might simply ignore it. But in peer networks with structured review processes, they can explore why the discrepancy exists and how to bridge it.
A teacher receives AI-generated lesson plans that assume resources her school lacks. Through her network’s collaborative problem-solving process, she finds teachers in similar situations who have created innovative adaptations. Together, they develop approaches that preserve AI’s pedagogical insights while working within real constraints.
The new architecture of collaborative learning
Working effectively with AI requires new forms of human collaboration built on three essential elements:
Reciprocal knowledge flows
When everyone has access to AI expertise, the most valuable learning happens between peers who share similar contexts and challenges. They teach each other not what AI knows, but how to make AI knowledge useful in their specific situations through:
- Structured project development and peer review;
- Regular assemblies where practitioners share experiences;
- Documentation of successful adaptations and failures;
- Continuous refinement based on collective feedback.
Structured experimentation
Peer networks provide safe spaces to experiment with AI collaboration. Through structured cycles of action and reflection, practitioners:
- Try AI recommendations in controlled ways;
- Document what works and what needs adaptation using shared frameworks;
- Share failures as valuable learning opportunities through facilitated sessions;
- Build collective knowledge about human-AI collaboration.
Continuous capability building
As AI capabilities evolve rapidly, no individual can keep pace alone. Peer networks create continuous learning environments where:
- Early adopters share new AI features through structured presentations;
- Groups explore emerging capabilities together in hands-on sessions;
- Collective intelligence about AI use grows through documented experiences;
- Everyone stays current through shared discovery and regular dialogue.
Evidence-based speculation: imagining peer networks that include both machines and humans
While the following examples are speculative, they build on current evidence from existing peer learning networks and emerging AI capabilities to imagine near-future possibilities.
The Nigerian immunization scenario
Based on Nigeria’s successful peer learning initiatives and current AI development trajectories, we can envision how AI-assisted immunization programs might work. AI could help identify optimal vaccine distribution patterns and predict which communities are at risk. Success would come when health workers form peer networks to share:
- Techniques for presenting AI predictions to community leaders effectively;
- Methods for adapting AI-suggested schedules to local market days and religious observances;
- Strategies for using AI insights while maintaining personal relationships that drive vaccine acceptance.
This scenario extrapolates from current successes in peer learning for immunization in Nigeria to imagine enhanced outcomes with AI partnership.
Climate health innovation networks
Drawing from existing climate health responses and AI’s growing environmental analysis capabilities, we can project how peer networks might function. As climate change creates unprecedented health challenges, AI models will predict impacts and suggest interventions. Community-based health workers could connect these ‘big data’ insights with their own local observations and experience to take action, sharing innovations like:
- Using AI climate predictions to prepare communities for heat waves;
- Adapting AI-suggested cooling strategies to local housing conditions;
- Combining traditional knowledge with AI insights for water management.
These possibilities build on documented peer learning successes in sharing health workers observations and insights about the impacts of climate change on the health of local communities.
Addressing AI’s limitations through collective wisdom
While AI offers powerful capabilities, we must acknowledge that technology is not neutral—AI systems carry biases from their training data, reflect the perspectives of their creators, and can perpetuate or amplify existing inequalities. Peer learning networks provide a crucial mechanism for identifying and addressing these limitations collectively.
Through structured dialogue and shared experiences, practitioners can:
- Document when AI recommendations reflect biases inappropriate for their contexts;
- Develop collective strategies for identifying and correcting AI biases;
- Share techniques for adapting AI outputs to ensure equity;
- Build shared understanding of AI’s limitations and appropriate use cases.
This collective vigilance and adaptation becomes essential for ensuring AI serves all communities fairly.
What this means for different stakeholders
For funders: Investing in collaborative capacity
The highest return on AI investment comes not from technology alone but from building human capacity to use it effectively. Peer learning networks:
- Multiply the impact of AI tools through shared adaptation strategies;
- Create sustainable capacity that grows with technological advancement;
- Generate innovations that improve AI applications for specific contexts;
- Build resilience through distributed expertise.
For practitioners: New collaborative competencies
Working effectively with AI requires skills best developed through structured peer learning:
- Partnership mindset: Seeing AI as a collaborative tool requiring human judgment.
- Adaptive expertise: Learning to blend AI capabilities with contextual knowledge.
- Reflective practice: Regularly examining what works in human-AI collaboration through structured reflection.
- Knowledge sharing: Contributing insights through peer review and dialogue that help others work better with AI.
For policymakers: Enabling collaborative ecosystems
Policies should support human-AI collaboration by:
- Funding peer learning infrastructure alongside AI deployment;
- Creating time and space for structured peer learning activities;
- Recognizing peer learning as essential professional development;
- Supporting documentation and spread of effective practices.
AI-human transformation through collaboration: A comparative view
Working with AI individuallyWorking with AI through structured peer networksPowerful tools but limited adaptation
Insights remain isolated
Success depends on individual skillContinuous adaptation through structured sharing
Insights multiply across network through peer review
Collective wisdom enhances individual capabilityAI recommendations may miss local context
Trial and error in isolation
Slow spread of effective practicesContext-aware applications emerge through dialogue
Structured experimentation with collective learning
Rapid diffusion through documented innovationsOverwhelmed by rapid AI changes
Struggling to keep pace alone
Uncertainty about appropriate useCollective sense-making through facilitated sessions
Shared discovery in peer projects
Growing confidence through structured supportThe collaborative future
As AI capabilities expand, two paths emerge:
Path 1: Individuals struggle alone to make sense of AI tools, leading to uneven adoption, missed opportunities, and growing inequality between those who figure it out and those who do not.
Path 2: Structured peer networks enable collective learning about human-AI collaboration, leading to widespread effective use, continuous innovation, and shared benefit from AI advances.
What determines outcomes is how humans organize to learn and work together with AI through structured peer learning processes.
María’s projected transformation
Six months after her initial struggles, we can envision how María’s experience might transform. Through structured peer learning—project development, peer review, and facilitated dialogue—she could learn to see AI not as a foreign expert imposing solutions, but as a knowledgeable colleague whose insights she can adapt and apply.
Based on current peer learning practices, she might discover techniques from colleagues across Latin America and the rest of the world:
- Methods for using AI diagnosis as a conversation starter with traditional healers;
- Strategies for validating AI recommendations through community health committees;
- Approaches for using AI analytics to support (not replace) community knowledge.
Following the pattern of peer learning networks, Maríawould begin contributing her own innovations through structured sharing, particularly around integrating AI insights with indigenous healing practices. Her documented approaches would spread through peer review and dialogue, helping thousands of health workers make AI truly useful in their communities.
Conclusion: The multiplication effect
AI transformation promises to augment human capabilities dramatically. Language interfaces will democratize access to advanced tools. Reasoning systems will provide expert-level analysis. Agentic AI will coordinate complex operations. These capabilities are beginning to transform what individuals can accomplish.
But the true multiplication effect will come through structured peer learning networks. When thousands of practitioners share how to work effectively with AI through systematic project work, peer review, and facilitated dialogue, they create collective intelligence about human-AI collaboration that no individual could develop alone. They transform AI from an impressive but alien technology into a natural extension of human capability.
For funders, this means the highest-impact investments combine AI tools with structured peer learning infrastructure. For policymakers, it means creating conditions where collaborative learning flourishes alongside technological deployment. For practitioners, it means embracing both AI partnership and peer collaboration through structured processes as essential to professional practice.
The future of human progress may rest on our ability to find effective ways to build powerful collaboration in networks that combine human and artificial intelligence. When we learn together through structured peer learning how to work with AI, we multiply not just individual capability but collective capacity to address the complex challenges facing our world.
AI is still emergent, changing constantly and rapidly. The peer learning methods are proven: we know a lot about how humans learn and collaborate. The question is how quickly we can scale this collaborative approach to match the pace of AI advancement. In that race, structured peer learning is not optional—it is essential.
Image: The Geneva Learning Foundation Collection © 2025
#ArtificialIntelligence #climateAndHealth #globalHealth #peerLearning #SanFranciscoConsensus -
When funding shrinks, impact must grow: the economic case for peer learning networks
Humanitarian, global health, and development organizations confront an unprecedented crisis. Donor funding is in a downward spiral, while needs intensify across every sector. Organizations face stark choices: reduce programs, cut staff, or fundamentally transform how they deliver results.
Traditional capacity building models have become economically unsustainable. Technical assistance, expert-led workshops, international travel, and venue-based training are examples of high-cost, low-volume activities that organizations may no longer be able to afford.
Yet the need for learning, coordination, and adaptive capacity has never been greater.
The opportunity cost of inaction
Organizations that fail to adapt face systematic disadvantage. Traditional approaches cannot survive current funding constraints while maintaining effectiveness. Meanwhile, global challenges intensify: climate change drives new disease patterns; conflict disrupts health systems; demographic transitions strain capacity.
These complex, interconnected challenges require adaptive systems that respond at the speed and scale of emerging threats. Organizations continuing expensive, ineffective approaches will face programmatic obsolescence.
Working with governments and trusted partners that include UNICEF, WHO, Gates Foundation, Wellcome Trust, and Gavi (as part of the Zero-Dose Learning Hub), the Geneva Learning Foundation’s peer learning networks have consistently demonstrated they can deliver measurably superior outcomes while reducing costs by up to 86% compared to conventional approaches.
Peer learning networks offer both immediate financial relief and strategic positioning for long-term sustainability. The evidence spans nine years, 137 countries, and collaborations with the most credible institutions in global health, humanitarian response, and research.
The unsustainable economics of traditional capacity building
A comprehensive analysis reveals the structural inefficiencies of conventional approaches. Expert consultants command daily rates of $800 or more, plus travel expenses. International workshops may require $15,000-30,000 for venues alone. Participant travel and accommodation averages $2,000 per person. A standard 50-participant workshop costs upward of $200,000.
When factoring limited sustainability, the economics become even more problematic. Traditional approaches achieve measurable implementation by only 15-20% of participants within six months. This translates to effective costs of $10,000-20,000 per participant who actually implements new practices.
A rudimentary cost-benefit analysis demonstrates how peer learning networks restructure these economics fundamentally.
ComponentTraditional approachPeer learning networksEfficiency gainCost per participant$1,850$26786% reductionImplementation rate15-20%70-80%4x higher successDuration of engagement2-3 days90+ days30x longerPost-training supportNoneContinuous networkSustained capacityLearn more: Calculating the relative effectiveness of expert coaching, peer learning, and cascade training
Evidence of measurable impact at scale
Value for money requires clear attribution between investments and outcomes.
In January 2020, we compared outcomes between two groups. Both had intent to take action to achieve results. Health workers using structured peer learning were seven times more likely to implement effective strategies resulting in improved outcomes, compared to the other group that relied on conventional approaches.
What about speed and scale?
In July 2024, working with Nigeria’s National Primary Health Care Development Agency (NPHCDA) and UNICEF, we connected 4,300 health workers across all states and 300+ local government areas within two weeks. Over 600 local organizations including government facilities, civil society, faith-based groups, and private sector actors joined this Immunization Collaborative.
With two more weeks, participants produced 409 peer-reviewed root cause analyses. By Week 6, we began to receive credible vaccination coverage improvements after six weeks, especially in conflict-affected northern regions where conventional approaches had consistently failed. The total programme cost was equivalent to 1.5 traditional workshops for 75 participants. Follow-up has shown that more than half of the participants are staying connected long after TGLF’s “jumpstarting” activities, driven by intrinsic motivation.
Côte d’Ivoire demonstrates crisis response capability. Working with Gavi and the Ministry of Health, we recruited 501 health workers from 96 districts (85% of the country) in nine days ahead of the country’s COVID-19 vaccination campaign in November 2021. Connected to each other, they shared local solutions and supported each other, contributing to vaccination of an additional 3.5 million additional people at $0.26 per vaccination delivered.
TGLF’s model empowers health workers to share knowledge, solve local challenges, and implement solutions via a digital platform. Unlike top-down training and technical assistance, it fosters collective intelligence, enabling rapid adaptation to crises. Since 2016, TGLF has mobilized networks for immunization, COVID-19 response, neglected tropical diseases (NTDs), mental health and psychosocial support, noncommunicable diseases, and climate-health resilience.
These cases illustrate the ability of TGLF’s model to address strategic global priorities—equity, resilience, and crisis response—while maximizing efficiency. This model offers a scalable, low-cost alternative that delivers measurable impact across diverse priorities.
Our mission is to share such breakthroughs with other organizations and networks that are willing to try new approaches.
Resource allocation for maximum efficiency
Our partnership analysis reveals optimal resource allocation patterns that maximize impact while minimizing cost:
- Human resources (85%): Action-focused approach leveraging human facilitation to foster trust, grow leadership capabilties, and nurture networks with a single-minded goal of supporting implementation to rapidly and sustainably achieve tangible outcomes.
- Digital infrastructure (10%): Scalable platform development enabling unlimited concurrent participants across multiple countries.
- Travel (5%): Minimal compared to 45% in traditional approaches, limited to essential coordination where social norms require face-to-face meetings, for example in partnership engagement with governments.
This structure enables remarkable economies of scale. While traditional approaches face increasing per-participant costs, peer learning networks demonstrate decreasing unit costs with growth. Global initiatives reaching 20,000+ participants across 60+ countries operate with per-participant costs under $10.
Sustainability through combined government and civil society ownership
Sustainability is critical amidst funding cuts. TGLF’s networks embed organically within government systems, involving both central planners in the capital as well as implementers across the country, at all levels of the health system.
Country ownership: Programs work within existing health system structures and national plans. Networks include 50% government staff and 80% district/community-level practitioners—the people who actually deliver services. In Nigeria, 600+ local organizations – both private and public – collaborated, embedding learning in both civil society and government structures.
Sustainability: In Côte d’Ivoire, 82% sustained engagement without incentives, fostering self-reliant networks. 78% said they no longer needed any assistance from TGLF to continue.
This approach enhances aid effectiveness, reducing dependency on external funding.
Aid effectiveness: Rather than bypassing systems, peer learning strengthens existing infrastructure. Networks continue functioning when external funding decreases because they operate through established government channels linked to civil society networks.
Transparency: Digital platforms create comprehensive audit trails providing unprecedented visibility into program implementation and results for donor oversight.
Implementation pathways for resource-constrained organizations
Organizations can adopt peer learning approaches through flexible pathways designed for immediate deployment.
- Rapid response initiatives (2-6 weeks to results): Address critical challenges requiring immediate mobilization. Suitable for disease outbreaks, humanitarian emergencies, or longer-term policy implementation.
- Program transformation (3-6 months): Convert existing technical assistance programs to peer learning models, typically reducing costs by 80-90% while expanding reach, inclusion, and outcomes.
- Cross-portfolio integration: Single platform investments serve multiple technical areas and geographic regions simultaneously, maximizing efficiency across donor portfolios with marginal costs approaching zero for additional countries or topics.
The strategic choice
The funding environment will not improve. Economic uncertainty in traditional donor countries, competing domestic priorities, and growing skepticism about aid effectiveness create permanent pressure for better value for money.
Organizations face a fundamental choice: continue expensive approaches with limited impact, or transition to emergent models that have already shown they can achieve superior results at dramatically lower cost while building lasting capability.
The question is not whether to change—budget constraints mandate adaptation. The question is whether organizations will choose approaches that thrive under resource constraints or continue hoping that some donors will fill the gaping holes left by funding cuts.
The evidence demonstrates that peer learning networks achieve 86% cost reduction while delivering 4x implementation rates and 30x longer engagement. These gains are not theoretical—they represent verified outcomes from active partnerships with leading global institutions.
In an era of permanent resource constraints and intensifying challenges, organizations that embrace this transformation will maximize their mission impact. Those that do not will find themselves increasingly unable to serve the communities that depend on their work.
Image: The Geneva Learning Foundation Collection © 2025
#costBenefitAnalysis #fundingCrisis #globalHealth #peerLearning #TheGenevaLearningFoundation #USAID #valueForMoney
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When funding shrinks, impact must grow: the economic case for peer learning networks
Humanitarian, global health, and development organizations confront an unprecedented crisis. Donor funding is in a downward spiral, while needs intensify across every sector. Organizations face stark choices: reduce programs, cut staff, or fundamentally transform how they deliver results.
Traditional capacity building models have become economically unsustainable. Technical assistance, expert-led workshops, international travel, and venue-based training are examples of high-cost, low-volume activities that organizations may no longer be able to afford.
Yet the need for learning, coordination, and adaptive capacity has never been greater.
The opportunity cost of inaction
Organizations that fail to adapt face systematic disadvantage. Traditional approaches cannot survive current funding constraints while maintaining effectiveness. Meanwhile, global challenges intensify: climate change drives new disease patterns; conflict disrupts health systems; demographic transitions strain capacity.
These complex, interconnected challenges require adaptive systems that respond at the speed and scale of emerging threats. Organizations continuing expensive, ineffective approaches will face programmatic obsolescence.
Working with governments and trusted partners that include UNICEF, WHO, Gates Foundation, Wellcome Trust, and Gavi (as part of the Zero-Dose Learning Hub), the Geneva Learning Foundation’s peer learning networks have consistently demonstrated they can deliver measurably superior outcomes while reducing costs by up to 86% compared to conventional approaches.
Peer learning networks offer both immediate financial relief and strategic positioning for long-term sustainability. The evidence spans nine years, 137 countries, and collaborations with the most credible institutions in global health, humanitarian response, and research.
The unsustainable economics of traditional capacity building
A comprehensive analysis reveals the structural inefficiencies of conventional approaches. Expert consultants command daily rates of $800 or more, plus travel expenses. International workshops may require $15,000-30,000 for venues alone. Participant travel and accommodation averages $2,000 per person. A standard 50-participant workshop costs upward of $200,000.
When factoring limited sustainability, the economics become even more problematic. Traditional approaches achieve measurable implementation by only 15-20% of participants within six months. This translates to effective costs of $10,000-20,000 per participant who actually implements new practices.
A rudimentary cost-benefit analysis demonstrates how peer learning networks restructure these economics fundamentally.
ComponentTraditional approachPeer learning networksEfficiency gainCost per participant$1,850$26786% reductionImplementation rate15-20%70-80%4x higher successDuration of engagement2-3 days90+ days30x longerPost-training supportNoneContinuous networkSustained capacityLearn more: Calculating the relative effectiveness of expert coaching, peer learning, and cascade training
Evidence of measurable impact at scale
Value for money requires clear attribution between investments and outcomes.
In January 2020, we compared outcomes between two groups. Both had intent to take action to achieve results. Health workers using structured peer learning were seven times more likely to implement effective strategies resulting in improved outcomes, compared to the other group that relied on conventional approaches.
What about speed and scale?
In July 2024, working with Nigeria’s National Primary Health Care Development Agency (NPHCDA) and UNICEF, we connected 4,300 health workers across all states and 300+ local government areas within two weeks. Over 600 local organizations including government facilities, civil society, faith-based groups, and private sector actors joined this Immunization Collaborative.
With two more weeks, participants produced 409 peer-reviewed root cause analyses. By Week 6, we began to receive credible vaccination coverage improvements after six weeks, especially in conflict-affected northern regions where conventional approaches had consistently failed. The total programme cost was equivalent to 1.5 traditional workshops for 75 participants. Follow-up has shown that more than half of the participants are staying connected long after TGLF’s “jumpstarting” activities, driven by intrinsic motivation.
Côte d’Ivoire demonstrates crisis response capability. Working with Gavi and the Ministry of Health, we recruited 501 health workers from 96 districts (85% of the country) in nine days ahead of the country’s COVID-19 vaccination campaign in November 2021. Connected to each other, they shared local solutions and supported each other, contributing to vaccination of an additional 3.5 million additional people at $0.26 per vaccination delivered.
TGLF’s model empowers health workers to share knowledge, solve local challenges, and implement solutions via a digital platform. Unlike top-down training and technical assistance, it fosters collective intelligence, enabling rapid adaptation to crises. Since 2016, TGLF has mobilized networks for immunization, COVID-19 response, neglected tropical diseases (NTDs), mental health and psychosocial support, noncommunicable diseases, and climate-health resilience.
These cases illustrate the ability of TGLF’s model to address strategic global priorities—equity, resilience, and crisis response—while maximizing efficiency. This model offers a scalable, low-cost alternative that delivers measurable impact across diverse priorities.
Our mission is to share such breakthroughs with other organizations and networks that are willing to try new approaches.
Resource allocation for maximum efficiency
Our partnership analysis reveals optimal resource allocation patterns that maximize impact while minimizing cost:
- Human resources (85%): Action-focused approach leveraging human facilitation to foster trust, grow leadership capabilties, and nurture networks with a single-minded goal of supporting implementation to rapidly and sustainably achieve tangible outcomes.
- Digital infrastructure (10%): Scalable platform development enabling unlimited concurrent participants across multiple countries.
- Travel (5%): Minimal compared to 45% in traditional approaches, limited to essential coordination where social norms require face-to-face meetings, for example in partnership engagement with governments.
This structure enables remarkable economies of scale. While traditional approaches face increasing per-participant costs, peer learning networks demonstrate decreasing unit costs with growth. Global initiatives reaching 20,000+ participants across 60+ countries operate with per-participant costs under $10.
Sustainability through combined government and civil society ownership
Sustainability is critical amidst funding cuts. TGLF’s networks embed organically within government systems, involving both central planners in the capital as well as implementers across the country, at all levels of the health system.
Country ownership: Programs work within existing health system structures and national plans. Networks include 50% government staff and 80% district/community-level practitioners—the people who actually deliver services. In Nigeria, 600+ local organizations – both private and public – collaborated, embedding learning in both civil society and government structures.
Sustainability: In Côte d’Ivoire, 82% sustained engagement without incentives, fostering self-reliant networks. 78% said they no longer needed any assistance from TGLF to continue.
This approach enhances aid effectiveness, reducing dependency on external funding.
Aid effectiveness: Rather than bypassing systems, peer learning strengthens existing infrastructure. Networks continue functioning when external funding decreases because they operate through established government channels linked to civil society networks.
Transparency: Digital platforms create comprehensive audit trails providing unprecedented visibility into program implementation and results for donor oversight.
Implementation pathways for resource-constrained organizations
Organizations can adopt peer learning approaches through flexible pathways designed for immediate deployment.
- Rapid response initiatives (2-6 weeks to results): Address critical challenges requiring immediate mobilization. Suitable for disease outbreaks, humanitarian emergencies, or longer-term policy implementation.
- Program transformation (3-6 months): Convert existing technical assistance programs to peer learning models, typically reducing costs by 80-90% while expanding reach, inclusion, and outcomes.
- Cross-portfolio integration: Single platform investments serve multiple technical areas and geographic regions simultaneously, maximizing efficiency across donor portfolios with marginal costs approaching zero for additional countries or topics.
The strategic choice
The funding environment will not improve. Economic uncertainty in traditional donor countries, competing domestic priorities, and growing skepticism about aid effectiveness create permanent pressure for better value for money.
Organizations face a fundamental choice: continue expensive approaches with limited impact, or transition to emergent models that have already shown they can achieve superior results at dramatically lower cost while building lasting capability.
The question is not whether to change—budget constraints mandate adaptation. The question is whether organizations will choose approaches that thrive under resource constraints or continue hoping that some donors will fill the gaping holes left by funding cuts.
The evidence demonstrates that peer learning networks achieve 86% cost reduction while delivering 4x implementation rates and 30x longer engagement. These gains are not theoretical—they represent verified outcomes from active partnerships with leading global institutions.
In an era of permanent resource constraints and intensifying challenges, organizations that embrace this transformation will maximize their mission impact. Those that do not will find themselves increasingly unable to serve the communities that depend on their work.
Image: The Geneva Learning Foundation Collection © 2025
#costBenefitAnalysis #fundingCrisis #globalHealth #peerLearning #TheGenevaLearningFoundation #USAID #valueForMoney
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Global health: learning to do more with less
In a climate of funding uncertainty, what if the most cost-effective investments in global health weren’t about supplies or infrastructure, but human networks that turn learning into action? In this short review article, we explore how peer learning networks that connect human beings to learn from and support each other can transform health outcomes with minimal resources.
The common thread uniting the different themes below reveals a powerful principle for our resource-constrained era: structured peer learning networks consistently deliver outsized impact relative to their cost.
Whether connecting health workers battling vaccine hesitancy in rural communities, maintaining essential immunization services during a global pandemic, supporting practitioners helping traumatized Ukrainian children, integrating AI tools ethically, or amplifying women’s voices from the frontlines – each case demonstrates how connecting practitioners across geographical and hierarchical boundaries transforms individual knowledge into collective action.
When health systems face funding shortfalls, these examples suggest that investing in human knowledge networks may be the most efficient approach available: they adapt to local contexts, identify solutions that work without additional resources, spread innovations rapidly, and build resilience that extends beyond any single intervention.
As one practitioner noted, “There’s a lot of trust in our network” – a resource that, unlike material supplies, grows stronger the more it’s used.
Sustaining gains in HPV vaccination coverage without additional resources
Recent analysis from TGLF’s Teach to Reach programme is providing valuable insights that both confirm and extend our understanding about what drives successful vaccination campaigns.
“Through peer learning networks, we discovered, for example, that tribal communities may show less vaccine hesitancy than urban populations, teachers could be more influential than health workers in driving vaccination acceptance, and religious institutions can become powerful allies,” explains TGLF’s Charlotte Mbuh. Other strategies include cancer survivors serving as advocates, WhatsApp groups connecting community health workers, and schoolchildren becoming effective messengers to initiate family conversations about vaccination
TGLF’s findings are based on analysis of implementation strategies shared by over 16,000 health professionals. Because they emerged through peer learning activities, participants got an immediate benefit. Now the real question is whether global partners and funders are recognize the significance and value of such field-based insights.
Most remarkably, analysis revealed that “success was often independent of resource levels” and “informal networks proved more important than formal ones” in sustaining high HPV vaccination coverage – suggesting that alongside material inputs, knowledge connections play a critical and often undervalued role.
Read the full article: HPV vaccination: New learning and leadership to bridge the gap between planning and implementation
5 years on: what the COVID-19 Peer Hub taught us about pandemic preparedness
When routine immunization services faced severe disruption in 2020, placing over 80 million children at risk, TGLF and the Bill & Melinda Gates Foundation (BMGF) supported a digital network connecting more than 6,000 frontline health workers across Africa, Asia, and Latin America. The results demonstrate why knowledge networks matter during crises.
Within just 10 days, the network generated 1,200+ ideas and developed 700 peer-reviewed action plans. Most significantly, implementation rates were seven times higher than conventional approaches, with collaborative participants achieving 30% better outcomes in maintaining essential health services.
“This approach complemented traditional models by recognizing frontline workers as experts in their own contexts,” says Mbuh. Quantitative assessment showed structured peer learning achieved efficacy scores of 3.2 on a 4-point scale, compared to 1.4 for traditional cascade training – providing evidence that practitioners benefit from both expert guidance and structured horizontal connections.
Read the full article: How can we reliably spread evidence-based practices at the speed and scale modern health challenges demand?
Peer learning for Psychological First Aid: Supporting Ukrainian children
The EU-funded programme on Psychological First Aid (PFA) for children affected by the humanitarian crisis in Ukraine reveals how peer learning creates value that enhances technical training.
During a recent ChildHub webinar, TGLF’s Reda Sadki outlined five unique benefits practitioners gain: contextual wisdom that complements standardized guidance, pattern recognition across diverse cases, validation of experiential knowledge, real-time problem-solving for urgent challenges, and professional resilience in difficult circumstances.
One practitioner, Serhii Federov, helped a frightened girl during rocket strikes by focusing on her teddy bear – illustrating how field adaptations enrich formal protocols. Another noted: “There is a lot of trust in our network,” highlighting how sharing experiences reduces isolation while building technical capacity.
With multiple entry points from microlearning modules to intensive peer learning exercises, this programme demonstrates how even in active crisis zones, structured knowledge sharing can deliver immediate improvements in service quality.
Artificial Intelligence as co-worker: Redefining power in global health
As technological tools transform global health practice, a new thought-provoking podcast (led, of course, by Artificial Intelligence hosts) examines how AI could reshape knowledge production in resource-constrained settings.
Based on TGLF’s Reda Sadki’s new article and framework for AI in global health, the podcast uses a specific case study to explore the “transparency paradox” practitioners face – navigating how to incorporate AI tools within existing global health accountability structures.
The podcast outlines TGLF’s framework for integrating AI responsibly in global health contexts, emphasizing: “It’s not about replacing human expertise, it’s about making it stronger.” This approach prioritizes local context and community empowerment while ensuring ethical considerations remain central.
As technological adoption accelerates across global health settings, frameworks that recognize existing dynamics become increasingly essential for ensuring equitable benefits.
Read the full article: Artificial intelligence, accountability, and authenticity: knowledge production and power in global health crisis
Women inspiring women: Amplifying voices from the frontlines
The “Women Inspiring Women” initiative amplifies the experiences of 177 women health workers from Africa, Asia, and Latin America through both a published book and peer learning course launched on International Women’s Day (IWD).
These women share personal stories and advice written as letters to their daughters, offering unique perspectives from cities, villages, refugee camps, and conflict zones. Dr. Eugenia Norah Chigamane from Malawi writes: “Pursuing a career in health work is not for the faint hearted,” while Kinda Ida Louise, a midwife from Burkina Faso, advises: “Never give up in the face of obstacles and difficulties, because there is always a positive point in every situation.”
The initiative follows TGLF’s proven methodology: immersion in stories, personal reflection, peer exchange, and developing action plans – transforming personal narratives into structured learning that drives institutional change. With women forming two-thirds of the global health workforce yet remaining underrepresented in leadership, this approach addresses both individual empowerment and systemic transformation.
Get the book “Women inspiring women” and enroll in the free learning course here.
As we face an era of unprecedented funding constraints in global health, these examples demonstrate a powerful truth: networked learning approaches consistently deliver remarkable outcomes across diverse contexts.
By connecting practitioners across boundaries, The Geneva Learning Foundation facilitates the transformation of individual knowledge into collective action – creating the resilience and adaptability our health systems urgently need.
The evidence is compelling: investing in human knowledge networks may be among the most efficient pathways to sustainable health impact.
Image: The Geneva Learning Foundation Collection © 2025
#fundingCrisis #globalDonors #globalHealth #healthEconomics #healthFinancing #TheGenevaLearningFoundation
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Global health: learning to do more with less
In a climate of funding uncertainty, what if the most cost-effective investments in global health weren’t about supplies or infrastructure, but human networks that turn learning into action? In this short review article, we explore how peer learning networks that connect human beings to learn from and support each other can transform health outcomes with minimal resources.
The common thread uniting the different themes below reveals a powerful principle for our resource-constrained era: structured peer learning networks consistently deliver outsized impact relative to their cost.
Whether connecting health workers battling vaccine hesitancy in rural communities, maintaining essential immunization services during a global pandemic, supporting practitioners helping traumatized Ukrainian children, integrating AI tools ethically, or amplifying women’s voices from the frontlines – each case demonstrates how connecting practitioners across geographical and hierarchical boundaries transforms individual knowledge into collective action.
When health systems face funding shortfalls, these examples suggest that investing in human knowledge networks may be the most efficient approach available: they adapt to local contexts, identify solutions that work without additional resources, spread innovations rapidly, and build resilience that extends beyond any single intervention.
As one practitioner noted, “There’s a lot of trust in our network” – a resource that, unlike material supplies, grows stronger the more it’s used.
Sustaining gains in HPV vaccination coverage without additional resources
Recent analysis from TGLF’s Teach to Reach programme is providing valuable insights that both confirm and extend our understanding about what drives successful vaccination campaigns.
“Through peer learning networks, we discovered, for example, that tribal communities may show less vaccine hesitancy than urban populations, teachers could be more influential than health workers in driving vaccination acceptance, and religious institutions can become powerful allies,” explains TGLF’s Charlotte Mbuh. Other strategies include cancer survivors serving as advocates, WhatsApp groups connecting community health workers, and schoolchildren becoming effective messengers to initiate family conversations about vaccination
TGLF’s findings are based on analysis of implementation strategies shared by over 16,000 health professionals. Because they emerged through peer learning activities, participants got an immediate benefit. Now the real question is whether global partners and funders are recognize the significance and value of such field-based insights.
Most remarkably, analysis revealed that “success was often independent of resource levels” and “informal networks proved more important than formal ones” in sustaining high HPV vaccination coverage – suggesting that alongside material inputs, knowledge connections play a critical and often undervalued role.
Read the full article: HPV vaccination: New learning and leadership to bridge the gap between planning and implementation
5 years on: what the COVID-19 Peer Hub taught us about pandemic preparedness
When routine immunization services faced severe disruption in 2020, placing over 80 million children at risk, TGLF and the Bill & Melinda Gates Foundation (BMGF) supported a digital network connecting more than 6,000 frontline health workers across Africa, Asia, and Latin America. The results demonstrate why knowledge networks matter during crises.
Within just 10 days, the network generated 1,200+ ideas and developed 700 peer-reviewed action plans. Most significantly, implementation rates were seven times higher than conventional approaches, with collaborative participants achieving 30% better outcomes in maintaining essential health services.
“This approach complemented traditional models by recognizing frontline workers as experts in their own contexts,” says Mbuh. Quantitative assessment showed structured peer learning achieved efficacy scores of 3.2 on a 4-point scale, compared to 1.4 for traditional cascade training – providing evidence that practitioners benefit from both expert guidance and structured horizontal connections.
Read the full article: How can we reliably spread evidence-based practices at the speed and scale modern health challenges demand?
Peer learning for Psychological First Aid: Supporting Ukrainian children
The EU-funded programme on Psychological First Aid (PFA) for children affected by the humanitarian crisis in Ukraine reveals how peer learning creates value that enhances technical training.
During a recent ChildHub webinar, TGLF’s Reda Sadki outlined five unique benefits practitioners gain: contextual wisdom that complements standardized guidance, pattern recognition across diverse cases, validation of experiential knowledge, real-time problem-solving for urgent challenges, and professional resilience in difficult circumstances.
One practitioner, Serhii Federov, helped a frightened girl during rocket strikes by focusing on her teddy bear – illustrating how field adaptations enrich formal protocols. Another noted: “There is a lot of trust in our network,” highlighting how sharing experiences reduces isolation while building technical capacity.
With multiple entry points from microlearning modules to intensive peer learning exercises, this programme demonstrates how even in active crisis zones, structured knowledge sharing can deliver immediate improvements in service quality.
Artificial Intelligence as co-worker: Redefining power in global health
As technological tools transform global health practice, a new thought-provoking podcast (led, of course, by Artificial Intelligence hosts) examines how AI could reshape knowledge production in resource-constrained settings.
Based on TGLF’s Reda Sadki’s new article and framework for AI in global health, the podcast uses a specific case study to explore the “transparency paradox” practitioners face – navigating how to incorporate AI tools within existing global health accountability structures.
The podcast outlines TGLF’s framework for integrating AI responsibly in global health contexts, emphasizing: “It’s not about replacing human expertise, it’s about making it stronger.” This approach prioritizes local context and community empowerment while ensuring ethical considerations remain central.
As technological adoption accelerates across global health settings, frameworks that recognize existing dynamics become increasingly essential for ensuring equitable benefits.
Read the full article: Artificial intelligence, accountability, and authenticity: knowledge production and power in global health crisis
Women inspiring women: Amplifying voices from the frontlines
The “Women Inspiring Women” initiative amplifies the experiences of 177 women health workers from Africa, Asia, and Latin America through both a published book and peer learning course launched on International Women’s Day (IWD).
These women share personal stories and advice written as letters to their daughters, offering unique perspectives from cities, villages, refugee camps, and conflict zones. Dr. Eugenia Norah Chigamane from Malawi writes: “Pursuing a career in health work is not for the faint hearted,” while Kinda Ida Louise, a midwife from Burkina Faso, advises: “Never give up in the face of obstacles and difficulties, because there is always a positive point in every situation.”
The initiative follows TGLF’s proven methodology: immersion in stories, personal reflection, peer exchange, and developing action plans – transforming personal narratives into structured learning that drives institutional change. With women forming two-thirds of the global health workforce yet remaining underrepresented in leadership, this approach addresses both individual empowerment and systemic transformation.
Get the book “Women inspiring women” and enroll in the free learning course here.
As we face an era of unprecedented funding constraints in global health, these examples demonstrate a powerful truth: networked learning approaches consistently deliver remarkable outcomes across diverse contexts.
By connecting practitioners across boundaries, The Geneva Learning Foundation facilitates the transformation of individual knowledge into collective action – creating the resilience and adaptability our health systems urgently need.
The evidence is compelling: investing in human knowledge networks may be among the most efficient pathways to sustainable health impact.
Image: The Geneva Learning Foundation Collection © 2025
#fundingCrisis #globalDonors #globalHealth #healthEconomics #healthFinancing #TheGenevaLearningFoundation
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Online learning completion rates in context: Rethinking success in digital learning networks
The comprehensive analysis of 221 Massive Open Online Courses (MOOCs) by Katy Jordan provides crucial insights for health professionals navigating the rapidly evolving landscape of digital learning. Her study, published in the International Review of Research in Open and Distributed Learning, examined completion rates across diverse platforms including Coursera, Open2Study, and others from 78 institutions.
- With median completion rates of just 12.6% (ranging from 0.7% to 52.1%), traditional metrics may suggest disappointment. Jordan’s multiple regression analysis revealed that while total enrollments have decreased over time, completion rates have actually increased.
- The data showed striking patterns in how participants engage, with the first and second weeks proving critical, after which the proportion of active students and those submitting assessments remains remarkably stable, with less than 3% difference between them.
- The research challenges common assumptions about “lurking” as a participation strategy and provides compelling evidence that course design factors significantly impact learning outcomes.
These findings reveal important patterns that can transform how we approach professional learning in global health contexts.
Beyond traditional completion metrics
For global health epidemiologists accustomed to face-to-face training with financial incentives and dedicated time away from work, these completion rates might initially appear appalling. In traditional capacity building programs, participants receive per diems, travel stipends, and paid time away from work. They are removed from their work environment, and their presence in the activities is often assumed to be evidence of both participation (often without any actual process metrics) and learning (with measurement often limited to “smile sheets” that measure sentiment or intent, not learning). Outcomes such as “completion” are rarely measured. Instead, attendance remains the key metric. In fact, completion rates are often confused with attendance. From this perspective, even the highest reported MOOC completion rate of 52.1% could be interpreted as a dismal failure.
However, this interpretation fundamentally misunderstands the different dynamics at play in digital learning environments. Unlike traditional training where external incentives and protected time create artificial conditions for participation, MOOCs operate in the reality of participants’ everyday professional lives. They typically do not require participants to stop work in order to learn, for example. The fact that up to half of enrollees in some courses complete them despite competing priorities, no financial incentives, and no dedicated work time represents remarkable commitment rather than failure.
What drives completion?
The accumulating evidence from MOOCs reveals three significant factors affecting completion:
- Course length: Shorter courses consistently achieved higher completion rates.
- Assessment type: Auto-grading showed better completion than peer assessment.
- Start date: More recent courses demonstrated higher completion rates.
The critical engagement period occurs within the first two weeks, after which participant behavior stabilizes.
This insight aligns with what emerging networked learning approaches have demonstrated in practice.
Rather than judging digital learning by metrics designed for classroom settings, we must recognize that participation patterns may reflect authentic integration with professional practice.
The measure of success should not necessarily be focused solely on how many complete the formal course. Rather, we should be considering how learning connects to real-world problem-solving and contributes to sustained professional networks.
Moving beyond MOOCs: peer learning networks
The Geneva Learning Foundation’s learning-to-action model offers a distinctly different model from conventional MOOCs. While MOOCs typically deliver standardized content to individual learners who progress independently, the Foundation’s digital learning initiatives are fundamentally network-based and practice-oriented. Rather than focusing on content consumption, their approach creates structured environments where health professionals connect, collaborate, and co-create knowledge while addressing real challenges in their work.
These learning networks differ from MOOCs in several key ways:
- Participants engage primarily with peers rather than pre-recorded content.
- Learning is organized around actual workplace challenges rather than abstract concepts.
- The experience builds sustainable professional relationships rather than one-time course completion.
- Assessment occurs through peer review and real-world application rather than quizzes or assignments.
- Structure is provided through facilitation and process rather than predetermined pathways.
The Foundation’s experience with over 60,000 health professionals across 137 countries demonstrates that when learning is connected to practice through networked approaches, different metrics of success emerge:
- Knowledge application: Practitioners implement solutions directly in their contexts
- Network formation: Sustainable learning relationships develop beyond formal “courses”
- Knowledge creation: Participants contribute to collective understanding
- System impact: Changes cascade through health systems
Implications for global health training and learning
For epidemiologists and health professionals designing learning initiatives, these findings suggest several strategic shifts:
- Modular design: Create shorter, more connected learning units rather than lengthy courses.
- Real-world integration: Link learning directly to participants’ practice contexts.
- Peer engagement: Provide structured opportunities for health workers to learn from each other.
- Network building: Focus on creating sustainable learning communities rather than isolated training events.
The future of professional learning, beyond completion rates
The research and practice point to a fundamental evolution in how we approach professional learning in global health. Rather than replicating traditional per diem-driven training models online, the most effective approaches harness the power of networks, enabling health professionals to learn continuously through structured peer interaction.
This perspective helps explain why seemingly low completion rates should not necessarily be viewed as failure. When digital learning is designed to create lasting networks of practice, knowledge emerges through collaborative action. Completion metrics therefore capture only a fraction of the impact.
For health systems facing complex challenges that include climate change, pandemic response, and health workforce shortages, this networked approach to learning offers a promising path forward—one that transforms how knowledge is created, shared, and applied to improve health outcomes globally.
Reference
- Jordan, K., 2015. Massive open online course completion rates revisited: Assessment, length and attrition. IRRODL 16. https://doi.org/10.19173/irrodl.v16i3.2112
Sculpture: The Geneva Learning Foundation Collection © 2025
#climateAndHealth #completionRates #learningMetrics #MOOCs #networkedLearning #onlineEducation #onlineLearning #professionalLearning
-
Online learning completion rates in context: Rethinking success in digital learning networks
The comprehensive analysis of 221 Massive Open Online Courses (MOOCs) by Katy Jordan provides crucial insights for health professionals navigating the rapidly evolving landscape of digital learning. Her study, published in the International Review of Research in Open and Distributed Learning, examined completion rates across diverse platforms including Coursera, Open2Study, and others from 78 institutions.
- With median completion rates of just 12.6% (ranging from 0.7% to 52.1%), traditional metrics may suggest disappointment. Jordan’s multiple regression analysis revealed that while total enrollments have decreased over time, completion rates have actually increased.
- The data showed striking patterns in how participants engage, with the first and second weeks proving critical, after which the proportion of active students and those submitting assessments remains remarkably stable, with less than 3% difference between them.
- The research challenges common assumptions about “lurking” as a participation strategy and provides compelling evidence that course design factors significantly impact learning outcomes.
These findings reveal important patterns that can transform how we approach professional learning in global health contexts.
Beyond traditional completion metrics
For global health epidemiologists accustomed to face-to-face training with financial incentives and dedicated time away from work, these completion rates might initially appear appalling. In traditional capacity building programs, participants receive per diems, travel stipends, and paid time away from work. They are removed from their work environment, and their presence in the activities is often assumed to be evidence of both participation (often without any actual process metrics) and learning (with measurement often limited to “smile sheets” that measure sentiment or intent, not learning). Outcomes such as “completion” are rarely measured. Instead, attendance remains the key metric. In fact, completion rates are often confused with attendance. From this perspective, even the highest reported MOOC completion rate of 52.1% could be interpreted as a dismal failure.
However, this interpretation fundamentally misunderstands the different dynamics at play in digital learning environments. Unlike traditional training where external incentives and protected time create artificial conditions for participation, MOOCs operate in the reality of participants’ everyday professional lives. They typically do not require participants to stop work in order to learn, for example. The fact that up to half of enrollees in some courses complete them despite competing priorities, no financial incentives, and no dedicated work time represents remarkable commitment rather than failure.
What drives completion?
The accumulating evidence from MOOCs reveals three significant factors affecting completion:
- Course length: Shorter courses consistently achieved higher completion rates.
- Assessment type: Auto-grading showed better completion than peer assessment.
- Start date: More recent courses demonstrated higher completion rates.
The critical engagement period occurs within the first two weeks, after which participant behavior stabilizes.
This insight aligns with what emerging networked learning approaches have demonstrated in practice.
Rather than judging digital learning by metrics designed for classroom settings, we must recognize that participation patterns may reflect authentic integration with professional practice.
The measure of success should not necessarily be focused solely on how many complete the formal course. Rather, we should be considering how learning connects to real-world problem-solving and contributes to sustained professional networks.
Moving beyond MOOCs: peer learning networks
The Geneva Learning Foundation’s learning-to-action model offers a distinctly different model from conventional MOOCs. While MOOCs typically deliver standardized content to individual learners who progress independently, the Foundation’s digital learning initiatives are fundamentally network-based and practice-oriented. Rather than focusing on content consumption, their approach creates structured environments where health professionals connect, collaborate, and co-create knowledge while addressing real challenges in their work.
These learning networks differ from MOOCs in several key ways:
- Participants engage primarily with peers rather than pre-recorded content.
- Learning is organized around actual workplace challenges rather than abstract concepts.
- The experience builds sustainable professional relationships rather than one-time course completion.
- Assessment occurs through peer review and real-world application rather than quizzes or assignments.
- Structure is provided through facilitation and process rather than predetermined pathways.
The Foundation’s experience with over 60,000 health professionals across 137 countries demonstrates that when learning is connected to practice through networked approaches, different metrics of success emerge:
- Knowledge application: Practitioners implement solutions directly in their contexts
- Network formation: Sustainable learning relationships develop beyond formal “courses”
- Knowledge creation: Participants contribute to collective understanding
- System impact: Changes cascade through health systems
Implications for global health training and learning
For epidemiologists and health professionals designing learning initiatives, these findings suggest several strategic shifts:
- Modular design: Create shorter, more connected learning units rather than lengthy courses.
- Real-world integration: Link learning directly to participants’ practice contexts.
- Peer engagement: Provide structured opportunities for health workers to learn from each other.
- Network building: Focus on creating sustainable learning communities rather than isolated training events.
The future of professional learning, beyond completion rates
The research and practice point to a fundamental evolution in how we approach professional learning in global health. Rather than replicating traditional per diem-driven training models online, the most effective approaches harness the power of networks, enabling health professionals to learn continuously through structured peer interaction.
This perspective helps explain why seemingly low completion rates should not necessarily be viewed as failure. When digital learning is designed to create lasting networks of practice, knowledge emerges through collaborative action. Completion metrics therefore capture only a fraction of the impact.
For health systems facing complex challenges that include climate change, pandemic response, and health workforce shortages, this networked approach to learning offers a promising path forward—one that transforms how knowledge is created, shared, and applied to improve health outcomes globally.
Reference
- Jordan, K., 2015. Massive open online course completion rates revisited: Assessment, length and attrition. IRRODL 16. https://doi.org/10.19173/irrodl.v16i3.2112
Sculpture: The Geneva Learning Foundation Collection © 2025
#climateAndHealth #completionRates #learningMetrics #MOOCs #networkedLearning #onlineEducation #onlineLearning #professionalLearning
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What is networked learning?
Networked learning happens when people learn through connections with others facing similar challenges. Think about how market traders learn their business – not through formal classes, but by connecting with other traders, sharing tips, and learning from each other’s experiences. This natural way of learning through relationships is what networked learning tries to support.
5 key features of networked learning:
- Learning from peers: In networked learning, people learn as much or more from others doing similar work as they do from experts. A community health worker in one village might discover an effective way to increase vaccination rates that could help workers in other villages.
- Knowledge flows in all directions: Unlike traditional training where knowledge flows only from the top down, networked learning allows knowledge to move in all directions – from national programs to local clinics, between regions, and from local implementers up to policy makers.
- Connections create value: The relationships between people become valuable resources for solving problems. Having a network of colleagues to ask for advice or share experiences with helps everyone work more effectively.
- Crossing boundaries: Networked learning connects people who might not normally work together – like doctors, nurses, community health workers, and managers. These diverse connections bring together different perspectives and create new solutions.
- Building on existing relationships: People already learn from colleagues they trust. Networked learning strengthens these natural connections and creates new ones, expanding who people can learn from.
Why networked learning matters for health work:
Health systems are full of isolated practitioners who could benefit from each other’s knowledge:
- A nurse who developed an effective patient education approach
- A community health worker who found a way to reach remote households
- A clinic manager who improved medicine supply systems
- A doctor who adapted treatment guidelines for local conditions
Networked learning connects these isolated pockets of knowledge, allowing good ideas to spread and adapt across different contexts.
Unlike traditional training that pulls people away from their work for workshops, networked learning happens through ongoing connections that support everyday problem-solving. When health workers participate in networked learning, they gain access to a community of practice that continues to provide support long after formal training ends.
Networked learning doesn’t replace expertise, but it recognizes that valuable knowledge exists throughout the health system – not just at the top. By connecting this distributed knowledge, networked learning helps good practices spread more quickly and adapt more effectively to local needs.
-
What is networked learning?
Networked learning happens when people learn through connections with others facing similar challenges. Think about how market traders learn their business – not through formal classes, but by connecting with other traders, sharing tips, and learning from each other’s experiences. This natural way of learning through relationships is what networked learning tries to support.
5 key features of networked learning:
- Learning from peers: In networked learning, people learn as much or more from others doing similar work as they do from experts. A community health worker in one village might discover an effective way to increase vaccination rates that could help workers in other villages.
- Knowledge flows in all directions: Unlike traditional training where knowledge flows only from the top down, networked learning allows knowledge to move in all directions – from national programs to local clinics, between regions, and from local implementers up to policy makers.
- Connections create value: The relationships between people become valuable resources for solving problems. Having a network of colleagues to ask for advice or share experiences with helps everyone work more effectively.
- Crossing boundaries: Networked learning connects people who might not normally work together – like doctors, nurses, community health workers, and managers. These diverse connections bring together different perspectives and create new solutions.
- Building on existing relationships: People already learn from colleagues they trust. Networked learning strengthens these natural connections and creates new ones, expanding who people can learn from.
Why networked learning matters for health work:
Health systems are full of isolated practitioners who could benefit from each other’s knowledge:
- A nurse who developed an effective patient education approach
- A community health worker who found a way to reach remote households
- A clinic manager who improved medicine supply systems
- A doctor who adapted treatment guidelines for local conditions
Networked learning connects these isolated pockets of knowledge, allowing good ideas to spread and adapt across different contexts.
Unlike traditional training that pulls people away from their work for workshops, networked learning happens through ongoing connections that support everyday problem-solving. When health workers participate in networked learning, they gain access to a community of practice that continues to provide support long after formal training ends.
Networked learning doesn’t replace expertise, but it recognizes that valuable knowledge exists throughout the health system – not just at the top. By connecting this distributed knowledge, networked learning helps good practices spread more quickly and adapt more effectively to local needs.
-
What is a complex problem?
What is a complex problem and what do we need to tackle it?
Problems can be simple or complex.
Simple problems have a clear first step, a known answer, and steps you can follow to get the answer.
Complex problems do not have a single right answer.
They have many possible answers or no answer at all.
What makes complex problems really hard is that they can change over time.
They have lots of different pieces that connect in unexpected ways.
When you try to solve them, one piece changes another piece, which changes another piece.
It is hard to see all the effects of your actions.
When you do something to help, later on the problem might get worse anyway.
You have to keep adapting your ideas.
To solve complex problems, you need to be able to:
- Think about all the puzzle pieces and how they fit, even when you do not know what they all are.
- Come up with plans and change them when parts of the problem change.
- Think back on your problem solving to get better for next time.
The most important things are being flexible, watching how every change affects other things, and learning from experience.
Image: The Geneva Learning Foundation Collection © 2024
References
- Buchanan, R., 1992. Wicked problems in design thinking. Design issues 5–21.
- Camillus, J.C., 2008. Strategy as a wicked problem. Harvard business review 86, 98.
- Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., Siemens, G., 2023. Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education: Artificial Intelligence 4, 100138. https://doi.org/10.1016/j.caeai.2023.100138
- Rittel, H.W., Webber, M.M., 1973. Dilemmas in a general theory of planning. Policy sciences 4, 155–169.
#complexLearning #complexProblems #learningStrategy #pedagogy #wickedProblems
-
What is a complex problem?
What is a complex problem and what do we need to tackle it?
Problems can be simple or complex.
Simple problems have a clear first step, a known answer, and steps you can follow to get the answer.
Complex problems do not have a single right answer.
They have many possible answers or no answer at all.
What makes complex problems really hard is that they can change over time.
They have lots of different pieces that connect in unexpected ways.
When you try to solve them, one piece changes another piece, which changes another piece.
It is hard to see all the effects of your actions.
When you do something to help, later on the problem might get worse anyway.
You have to keep adapting your ideas.
To solve complex problems, you need to be able to:
- Think about all the puzzle pieces and how they fit, even when you do not know what they all are.
- Come up with plans and change them when parts of the problem change.
- Think back on your problem solving to get better for next time.
The most important things are being flexible, watching how every change affects other things, and learning from experience.
Image: The Geneva Learning Foundation Collection © 2024
References
- Buchanan, R., 1992. Wicked problems in design thinking. Design issues 5–21.
- Camillus, J.C., 2008. Strategy as a wicked problem. Harvard business review 86, 98.
- Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., Siemens, G., 2023. Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education: Artificial Intelligence 4, 100138. https://doi.org/10.1016/j.caeai.2023.100138
- Rittel, H.W., Webber, M.M., 1973. Dilemmas in a general theory of planning. Policy sciences 4, 155–169.
#complexLearning #complexProblems #learningStrategy #pedagogy #wickedProblems
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Artificial intelligence, accountability, and authenticity: knowledge production and power in global health crisis
I know and appreciate Joseph, a Kenyan health leader from Murang’a County, for years of diligent leadership and contributions as a Scholar of The Geneva Learning Foundation (TGLF). Recently, he began submitting AI-generated responses to Teach to Reach Questions that were meant to elicit narratives grounded in his personal experience.
Seemingly unrelated to this, OpenAI just announced plans for specialized AI agents—autonomous systems designed to perform complex cognitive tasks—with pricing ranging from $2,000 monthly for a “high-income knowledge worker” equivalent to $20,000 monthly for “PhD-level” research capabilities.
This is happening at a time when traditional funding structures in global health, development, and humanitarian response face unprecedented volatility.
These developments intersect around fundamental questions of knowledge economics, authenticity, and power in global health contexts.
I want to explore three questions:
- What happens when health professionals in resource-constrained settings experiment with AI technologies within accountability systems that often penalize innovation?
- How might systems claiming to replicate human knowledge work transform the economics and ethics of knowledge production?
- And how should we navigate the tensions between technological adoption and authentic knowledge creation?
Artificial intelligence within punitive accountability structures of global health
For years, Joseph had shared thoughtful, context-rich contributions based on his direct experiences. All of a sudden, he was submitting generic mush with all the trappings of bad generative AI content.
Should we interpret this as disengagement from peer learning?
Given his history of diligence and commitment, I could not dismiss his exploration of AI tools as diminished engagement. Instead, I understood it as an attempt to incorporate new capabilities into his professional repertoire. This was confirmed when I got to chat with him on a WhatsApp call.
Our current Teach to Reach Questions system has not yet incorporated the use of AI. Our “old” system did not provide any way for Joseph to communicate what he was exploring.
Hence, the quality limitations in AI-generated narratives highlight not ethical failings but a developmental process requiring support rather than judgment.
But what does this look like when situated within global health accountability structures?
Health workers frequently operate within highly punitive systems where performance evaluation directly impacts funding decisions. International donors maintain extensive surveillance of program implementation, creating environments where experimentation carries significant risk. When knowledge sharing becomes entangled with performance evaluation, the incentives for transparency about AI “co-working” (i.e., collaboration between human and AI in work) diminish dramatically.
Seen through this lens, the question becomes not whether to prohibit AI-generated contributions but how to create environments where practitioners can explore technological capabilities without fear that disclosure will lead to automatic devaluation of their knowledge, regardless of its substantive quality. This heavily depends on the learning culture, which remains largely ignored or dismissed in global health.
The transparency paradox: disclosure and devaluation of artificial intelligence in global health
This case illustrates what might be called the “transparency paradox”—when disclosure or recognition of AI contribution triggers automatic devaluation regardless of substantive quality. Current attitudes create a problematic binary: acknowledge AI assistance and have contributions dismissed regardless of quality, or withhold disclosure and risk accusations of misrepresentation or worse.
This paradox creates perverse incentives against transparency, particularly in contexts where knowledge production undergoes intensive evaluation linked to resource allocation. The global health sector’s evaluation systems often emphasize compliance over innovation, creating additional barriers to technological experimentation. When every submission potentially affects funding decisions, incentives for technological experimentation become entangled with accountability pressures.
This dynamic particularly affects practitioners in Global South contexts, who face more intense scrutiny while having less institutional protection for experimentation. The punitive nature of global health accountability systems deserves particular emphasis. Health workers operate within hierarchical structures where performance is consistently monitored by both national governments and international donors. Surveillance extends from quantitative indicators to qualitative assessments of knowledge and practice.
In environments where funding depends on demonstrating certain types of knowledge or outcomes, the incentive to leverage artificial intelligence in global health may conflict with values of authenticity and transparency. This surveillance culture creates uniquely challenging conditions for technological experimentation. When performance evaluation drives resource allocation decisions, health workers face considerable risk in acknowledging technological assistance—even as they face pressure to incorporate emerging technologies into their practice.
The economics of knowledge in global health contexts
OpenAI’s announced “agents” represent a substantial evolution beyond simple chatbots or language models. If they are able to deliver what they just announced, these specialized systems would autonomously perform complex tasks simulating the cognitive work of highly-skilled professionals. The most expensive tier, priced at $20,000 monthly, purportedly offers “PhD-level” research capabilities, working continuously without the limitations of human scheduling or attention.
These claims, while unproven, suggest a potential future where knowledge work economics fundamentally change. For global health organizations operating in Geneva, where even a basic intern position for a recent master’s degree graduate cost more than 200 times that of a ChatGPT subscription, the economic proposition of systems working 24/7 for potentially comparable costs merits careful examination.
However, the global health sector has historically operated with significant labor stratification, where personnel in Global North institutions command substantially higher compensation than those working in Global South contexts. Local health workers often provide critical knowledge at compensation rates far below those of international consultants or staff at Northern institutions. This creates a different economic equation than suggested by Geneva-based comparisons. Many organizations have long relied on substantially lower local labor costs, often justified through capacity-building narratives that mask underlying power asymmetries.
Given this history, the risk that artificial intelligence in global health would replace local knowledge workers might initially appear questionable. Furthermore, the sector has demonstrated considerable resistance to technological adoption, particularly when it might disrupt established operational patterns. However, this analysis overlooks how economic pressures interact with technological change during periods of significant disruption.
The recent decisions of many government to donors to suddenly and drastically cut funding and shut down programs illustrates how rapidly even established funding structures can collapse. In such environments, organizations face existential questions about maintaining operational capacity, potentially creating conditions where technological substitution becomes more attractive despite institutional resistance.
A new AI divide
ChatGPT and other generative AI tools were initially “geo-locked”, making them more difficult to access from outside Europe and North America.
Now, the stratified pricing structure of OpenAI’s announced agents raises profound equity concerns. With the most sophisticated capabilities reserved for those able to pay high costs for the most capable agents, we face the potential emergence of an “AI divide” that threatens to reinforce existing knowledge power imbalances.
This divide presents particular challenges for global health organizations working across diverse contexts. If advanced AI capabilities remain the exclusive province of Northern institutions while Southern partners operate with limited or no AI augmentation, how might this affect knowledge dynamics already characterized by significant inequities?
The AI divide extends beyond simple access to include quality differentials in available systems. Even as simple AI tools become widely available, sophisticated capabilities that genuinely enhance knowledge work may remain concentrated within well-resourced institutions. This could lead to a scenario where practitioners in resource-constrained settings use rudimentary AI tools that produce low-quality outputs, further reinforcing perceptions of capability gaps between North and South.
Confronting power dynamics in AI integration
Traditional knowledge systems in global health position expertise in academic and institutional centers, with information flowing outward to practitioners who implement standardized solutions. This existing structure reflects and reinforces global power imbalances.
The integration of AI within these systems could either exacerbate these inequities—by further concentrating knowledge production capabilities within well-resourced institutions—or potentially disrupt them by enabling more distributed knowledge creation processes.
Joseph’s journey demonstrates this tension. His adoption of AI tools might be viewed as an attempt to access capabilities otherwise reserved for those with greater institutional resources. The question becomes not whether to allow such adoption, but how to ensure it serves genuine knowledge democratization rather than simply producing more sophisticated simulations of participation.
These emerging dynamics require us to fundamentally rethink how knowledge is valued, created, and shared within global health networks. The transparency paradox, economic pressures, and emerging AI divide suggest that technological integration will not occur within neutral space but rather within contexts already characterized by significant power asymmetries.
Developing effective responses requires moving beyond simple prescriptions about AI adoption toward deeper analysis of how these technologies interact with existing power structures—and how they might be intentionally directed toward either reinforcing or transforming these structures.
My framework for Artificial Intelligence as co-worker to support networked learning and local action is intended to contribute to such efforts.
Illustration: The Geneva Learning Foundation Collection © 2025
References
Frehywot, S., Vovides, Y., 2024. Contextualizing algorithmic literacy framework for global health workforce education. AIH 0, 4903. https://doi.org/10.36922/aih.4903
Hazarika, I., 2020. Artificial intelligence: opportunities and implications for the health workforce. International Health 12, 241–245. https://doi.org/10.1093/inthealth/ihaa007
John, A., Newton-Lewis, T., Srinivasan, S., 2019. Means, Motives and Opportunity: determinants of community health worker performance. BMJ Glob Health 4, e001790. https://doi.org/10.1136/bmjgh-2019-001790
Newton-Lewis, T., Munar, W., Chanturidze, T., 2021. Performance management in complex adaptive systems: a conceptual framework for health systems. BMJ Glob Health 6, e005582. https://doi.org/10.1136/bmjgh-2021-005582
Newton-Lewis, T., Nanda, P., 2021. Problematic problem diagnostics: why digital health interventions for community health workers do not always achieve their desired impact. BMJ Glob Health 6, e005942. https://doi.org/10.1136/bmjgh-2021-005942
Artificial Intelligence and the health workforce: Perspectives from medical associations on AI in health (OECD Artificial Intelligence Papers No. 28), 2024. , OECD Artificial Intelligence Papers. https://doi.org/10.1787/9a31d8af-en
Sadki, R. (2025). A global health framework for Artificial Intelligence as co-worker to support networked learning and local action. Reda Sadki. https://doi.org/10.59350/gr56c-cdd51
#accountability #accountabilityOverloads #ArtificialIntelligence #compliance #conservatism #globalHealth #healthWorkers #HRH #incentives #innovation #learningCulture #performanceMonitoring #TeachToReach
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Artificial intelligence, accountability, and authenticity: knowledge production and power in global health crisis
I know and appreciate Joseph, a Kenyan health leader from Murang’a County, for years of diligent leadership and contributions as a Scholar of The Geneva Learning Foundation (TGLF). Recently, he began submitting AI-generated responses to Teach to Reach Questions that were meant to elicit narratives grounded in his personal experience.
Seemingly unrelated to this, OpenAI just announced plans for specialized AI agents—autonomous systems designed to perform complex cognitive tasks—with pricing ranging from $2,000 monthly for a “high-income knowledge worker” equivalent to $20,000 monthly for “PhD-level” research capabilities.
This is happening at a time when traditional funding structures in global health, development, and humanitarian response face unprecedented volatility.
These developments intersect around fundamental questions of knowledge economics, authenticity, and power in global health contexts.
I want to explore three questions:
- What happens when health professionals in resource-constrained settings experiment with AI technologies within accountability systems that often penalize innovation?
- How might systems claiming to replicate human knowledge work transform the economics and ethics of knowledge production?
- And how should we navigate the tensions between technological adoption and authentic knowledge creation?
Artificial intelligence within punitive accountability structures of global health
For years, Joseph had shared thoughtful, context-rich contributions based on his direct experiences. All of a sudden, he was submitting generic mush with all the trappings of bad generative AI content.
Should we interpret this as disengagement from peer learning?
Given his history of diligence and commitment, I could not dismiss his exploration of AI tools as diminished engagement. Instead, I understood it as an attempt to incorporate new capabilities into his professional repertoire. This was confirmed when I got to chat with him on a WhatsApp call.
Our current Teach to Reach Questions system has not yet incorporated the use of AI. Our “old” system did not provide any way for Joseph to communicate what he was exploring.
Hence, the quality limitations in AI-generated narratives highlight not ethical failings but a developmental process requiring support rather than judgment.
But what does this look like when situated within global health accountability structures?
Health workers frequently operate within highly punitive systems where performance evaluation directly impacts funding decisions. International donors maintain extensive surveillance of program implementation, creating environments where experimentation carries significant risk. When knowledge sharing becomes entangled with performance evaluation, the incentives for transparency about AI “co-working” (i.e., collaboration between human and AI in work) diminish dramatically.
Seen through this lens, the question becomes not whether to prohibit AI-generated contributions but how to create environments where practitioners can explore technological capabilities without fear that disclosure will lead to automatic devaluation of their knowledge, regardless of its substantive quality. This heavily depends on the learning culture, which remains largely ignored or dismissed in global health.
The transparency paradox: disclosure and devaluation of artificial intelligence in global health
This case illustrates what might be called the “transparency paradox”—when disclosure or recognition of AI contribution triggers automatic devaluation regardless of substantive quality. Current attitudes create a problematic binary: acknowledge AI assistance and have contributions dismissed regardless of quality, or withhold disclosure and risk accusations of misrepresentation or worse.
This paradox creates perverse incentives against transparency, particularly in contexts where knowledge production undergoes intensive evaluation linked to resource allocation. The global health sector’s evaluation systems often emphasize compliance over innovation, creating additional barriers to technological experimentation. When every submission potentially affects funding decisions, incentives for technological experimentation become entangled with accountability pressures.
This dynamic particularly affects practitioners in Global South contexts, who face more intense scrutiny while having less institutional protection for experimentation. The punitive nature of global health accountability systems deserves particular emphasis. Health workers operate within hierarchical structures where performance is consistently monitored by both national governments and international donors. Surveillance extends from quantitative indicators to qualitative assessments of knowledge and practice.
In environments where funding depends on demonstrating certain types of knowledge or outcomes, the incentive to leverage artificial intelligence in global health may conflict with values of authenticity and transparency. This surveillance culture creates uniquely challenging conditions for technological experimentation. When performance evaluation drives resource allocation decisions, health workers face considerable risk in acknowledging technological assistance—even as they face pressure to incorporate emerging technologies into their practice.
The economics of knowledge in global health contexts
OpenAI’s announced “agents” represent a substantial evolution beyond simple chatbots or language models. If they are able to deliver what they just announced, these specialized systems would autonomously perform complex tasks simulating the cognitive work of highly-skilled professionals. The most expensive tier, priced at $20,000 monthly, purportedly offers “PhD-level” research capabilities, working continuously without the limitations of human scheduling or attention.
These claims, while unproven, suggest a potential future where knowledge work economics fundamentally change. For global health organizations operating in Geneva, where even a basic intern position for a recent master’s degree graduate cost more than 200 times that of a ChatGPT subscription, the economic proposition of systems working 24/7 for potentially comparable costs merits careful examination.
However, the global health sector has historically operated with significant labor stratification, where personnel in Global North institutions command substantially higher compensation than those working in Global South contexts. Local health workers often provide critical knowledge at compensation rates far below those of international consultants or staff at Northern institutions. This creates a different economic equation than suggested by Geneva-based comparisons. Many organizations have long relied on substantially lower local labor costs, often justified through capacity-building narratives that mask underlying power asymmetries.
Given this history, the risk that artificial intelligence in global health would replace local knowledge workers might initially appear questionable. Furthermore, the sector has demonstrated considerable resistance to technological adoption, particularly when it might disrupt established operational patterns. However, this analysis overlooks how economic pressures interact with technological change during periods of significant disruption.
The recent decisions of many government to donors to suddenly and drastically cut funding and shut down programs illustrates how rapidly even established funding structures can collapse. In such environments, organizations face existential questions about maintaining operational capacity, potentially creating conditions where technological substitution becomes more attractive despite institutional resistance.
A new AI divide
ChatGPT and other generative AI tools were initially “geo-locked”, making them more difficult to access from outside Europe and North America.
Now, the stratified pricing structure of OpenAI’s announced agents raises profound equity concerns. With the most sophisticated capabilities reserved for those able to pay high costs for the most capable agents, we face the potential emergence of an “AI divide” that threatens to reinforce existing knowledge power imbalances.
This divide presents particular challenges for global health organizations working across diverse contexts. If advanced AI capabilities remain the exclusive province of Northern institutions while Southern partners operate with limited or no AI augmentation, how might this affect knowledge dynamics already characterized by significant inequities?
The AI divide extends beyond simple access to include quality differentials in available systems. Even as simple AI tools become widely available, sophisticated capabilities that genuinely enhance knowledge work may remain concentrated within well-resourced institutions. This could lead to a scenario where practitioners in resource-constrained settings use rudimentary AI tools that produce low-quality outputs, further reinforcing perceptions of capability gaps between North and South.
Confronting power dynamics in AI integration
Traditional knowledge systems in global health position expertise in academic and institutional centers, with information flowing outward to practitioners who implement standardized solutions. This existing structure reflects and reinforces global power imbalances.
The integration of AI within these systems could either exacerbate these inequities—by further concentrating knowledge production capabilities within well-resourced institutions—or potentially disrupt them by enabling more distributed knowledge creation processes.
Joseph’s journey demonstrates this tension. His adoption of AI tools might be viewed as an attempt to access capabilities otherwise reserved for those with greater institutional resources. The question becomes not whether to allow such adoption, but how to ensure it serves genuine knowledge democratization rather than simply producing more sophisticated simulations of participation.
These emerging dynamics require us to fundamentally rethink how knowledge is valued, created, and shared within global health networks. The transparency paradox, economic pressures, and emerging AI divide suggest that technological integration will not occur within neutral space but rather within contexts already characterized by significant power asymmetries.
Developing effective responses requires moving beyond simple prescriptions about AI adoption toward deeper analysis of how these technologies interact with existing power structures—and how they might be intentionally directed toward either reinforcing or transforming these structures.
My framework for Artificial Intelligence as co-worker to support networked learning and local action is intended to contribute to such efforts.
Illustration: The Geneva Learning Foundation Collection © 2025
References
Frehywot, S., Vovides, Y., 2024. Contextualizing algorithmic literacy framework for global health workforce education. AIH 0, 4903. https://doi.org/10.36922/aih.4903
Hazarika, I., 2020. Artificial intelligence: opportunities and implications for the health workforce. International Health 12, 241–245. https://doi.org/10.1093/inthealth/ihaa007
John, A., Newton-Lewis, T., Srinivasan, S., 2019. Means, Motives and Opportunity: determinants of community health worker performance. BMJ Glob Health 4, e001790. https://doi.org/10.1136/bmjgh-2019-001790
Newton-Lewis, T., Munar, W., Chanturidze, T., 2021. Performance management in complex adaptive systems: a conceptual framework for health systems. BMJ Glob Health 6, e005582. https://doi.org/10.1136/bmjgh-2021-005582
Newton-Lewis, T., Nanda, P., 2021. Problematic problem diagnostics: why digital health interventions for community health workers do not always achieve their desired impact. BMJ Glob Health 6, e005942. https://doi.org/10.1136/bmjgh-2021-005942
Artificial Intelligence and the health workforce: Perspectives from medical associations on AI in health (OECD Artificial Intelligence Papers No. 28), 2024. , OECD Artificial Intelligence Papers. https://doi.org/10.1787/9a31d8af-en
Sadki, R. (2025). A global health framework for Artificial Intelligence as co-worker to support networked learning and local action. Reda Sadki. https://doi.org/10.59350/gr56c-cdd51
#accountability #accountabilityOverloads #ArtificialIntelligence #compliance #conservatism #globalHealth #healthWorkers #HRH #incentives #innovation #learningCulture #performanceMonitoring #TeachToReach
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Peer learning for Psychological First Aid: New ways to strengthen support for Ukrainian children
This article is based on Reda Sadki’s presentation at the ChildHub “Webinar on Psychological First Aid for Children; Supporting the Most Vulnerable” on 6 March 2025. Learn more about the Certificate peer learning programme on Psychological First Aid (PFA) in support of children affected by the humanitarian crisis in Ukraine. Get insights from professionals who support Ukrainian children.
“I understood that if we want to cry, we can cry,” reflected a practitioner in the Certificate peer learning programme on Psychological First Aid (PFA) in support of children affected by the humanitarian crisis in Ukraine – illustrating the kind of personal transformation that complements technical training.
During the ChildHub “Webinar on Psychological First Aid for Children; Supporting the Most Vulnerable”, the Geneva Learning Foundation’s Reda Sadki explained how peer learning provides value that traditional training alone cannot deliver. The EU-funded program on Psychological First Aid (PFA) for children demonstrates that practitioners gain five specific benefits:
First, peer learning reveals contextual wisdom missing from standardized guidance. While technical training provides general principles, practitioners encounter varied situations requiring adaptation. When Serhii Federov helped a frightened girl during rocket strikes by focusing on her teddy bear, he discovered an approach not found in manuals: “This exercise helped the girl switch her focus from the situation around her to caring for the bear.”
Second, practitioners document pattern recognition across diverse cases. Sadki shared how analysis of practitioner experiences revealed that “PFA extends beyond emergency situations into everyday environments” and “children often invent their own therapeutic activities when given space.” These insights help practitioners recognize which approaches work in specific contexts.
Third, peer learning validates experiential knowledge. One practitioner described how simple acknowledgment of feelings often produced visible relief in children, while another found that basic physical comforts had significant psychological impact. These observations, when shared and confirmed across multiple practitioners, build confidence in approaches that might otherwise seem too simple.
Fourth, the network provides real-time problem-solving for urgent challenges. During fortnightly PFA Connect sessions, practitioners discuss immediate issues like “supporting children under three years” or “recognizing severe reactions requiring referrals.” As Sadki explained, these sessions produce concise “key learning points” summarizing practical solutions practitioners can immediately apply.
Finally, peer learning builds professional identity and resilience. “There’s a lot of trust in our network,” Sadki quoted from a participant, demonstrating how sharing experiences reduces isolation and builds a supportive community where practitioners can acknowledge their own emotions and challenges.
The webinar highlighted how this approach creates measurable impact, with practitioners developing case studies that transform tacit knowledge into documented evidence and structured feedback that helps discover blind spots in their practice.
For practitioners interested in joining, Sadki outlined multiple entry points from microlearning modules completed in under an hour to more intensive peer learning exercises, all designed to strengthen support to children while building practitioners’ own professional capabilities.
This project is funded by the European Union. Its contents are the sole responsibility of TGLF, and do not necessarily reflect the views of the European Union.
Illustration: The Geneva Learning Foundation Collection © 2025
#CertificatePeerLearningProgrammeOnPsychologicalFirstAidPFAInSupportOfChildrenAffectedByTheHumanitarianCrisisInUkraine #ChildHub #children #globalHealth #IFRC #InternationalFederationOfRedCrossAndRedCrescentSocietiesIFRC #MHPSS #peerLearning #PsychologicalFirstAidPFA #psychosocialSupport #TheGenevaLearningFoundation #Ukraine -
Peer learning for Psychological First Aid: New ways to strengthen support for Ukrainian children
This article is based on Reda Sadki’s presentation at the ChildHub “Webinar on Psychological First Aid for Children; Supporting the Most Vulnerable” on 6 March 2025. Learn more about the Certificate peer learning programme on Psychological First Aid (PFA) in support of children affected by the humanitarian crisis in Ukraine. Get insights from professionals who support Ukrainian children.
“I understood that if we want to cry, we can cry,” reflected a practitioner in the Certificate peer learning programme on Psychological First Aid (PFA) in support of children affected by the humanitarian crisis in Ukraine – illustrating the kind of personal transformation that complements technical training.
During the ChildHub “Webinar on Psychological First Aid for Children; Supporting the Most Vulnerable”, the Geneva Learning Foundation’s Reda Sadki explained how peer learning provides value that traditional training alone cannot deliver. The EU-funded program on Psychological First Aid (PFA) for children demonstrates that practitioners gain five specific benefits:
First, peer learning reveals contextual wisdom missing from standardized guidance. While technical training provides general principles, practitioners encounter varied situations requiring adaptation. When Serhii Federov helped a frightened girl during rocket strikes by focusing on her teddy bear, he discovered an approach not found in manuals: “This exercise helped the girl switch her focus from the situation around her to caring for the bear.”
Second, practitioners document pattern recognition across diverse cases. Sadki shared how analysis of practitioner experiences revealed that “PFA extends beyond emergency situations into everyday environments” and “children often invent their own therapeutic activities when given space.” These insights help practitioners recognize which approaches work in specific contexts.
Third, peer learning validates experiential knowledge. One practitioner described how simple acknowledgment of feelings often produced visible relief in children, while another found that basic physical comforts had significant psychological impact. These observations, when shared and confirmed across multiple practitioners, build confidence in approaches that might otherwise seem too simple.
Fourth, the network provides real-time problem-solving for urgent challenges. During fortnightly PFA Connect sessions, practitioners discuss immediate issues like “supporting children under three years” or “recognizing severe reactions requiring referrals.” As Sadki explained, these sessions produce concise “key learning points” summarizing practical solutions practitioners can immediately apply.
Finally, peer learning builds professional identity and resilience. “There’s a lot of trust in our network,” Sadki quoted from a participant, demonstrating how sharing experiences reduces isolation and builds a supportive community where practitioners can acknowledge their own emotions and challenges.
The webinar highlighted how this approach creates measurable impact, with practitioners developing case studies that transform tacit knowledge into documented evidence and structured feedback that helps discover blind spots in their practice.
For practitioners interested in joining, Sadki outlined multiple entry points from microlearning modules completed in under an hour to more intensive peer learning exercises, all designed to strengthen support to children while building practitioners’ own professional capabilities.
This project is funded by the European Union. Its contents are the sole responsibility of TGLF, and do not necessarily reflect the views of the European Union.
Illustration: The Geneva Learning Foundation Collection © 2025
#CertificatePeerLearningProgrammeOnPsychologicalFirstAidPFAInSupportOfChildrenAffectedByTheHumanitarianCrisisInUkraine #ChildHub #children #globalHealth #IFRC #InternationalFederationOfRedCrossAndRedCrescentSocietiesIFRC #MHPSS #peerLearning #PsychologicalFirstAidPFA #psychosocialSupport #TheGenevaLearningFoundation #Ukraine -
The cost of inaction: Quantifying the impact of climate change on health
This World Bank report ‘The Cost of Inaction: Quantifying the Impact of Climate Change on Health in Low- and Middle-Income Countries’ presents new analysis of climate change impacts on health systems and outcomes in the regions that are bearing the brunt of these impacts.
Key analytical insights to quantify climate change impacts on health
The report makes three contributions to our understanding of climate-health interactions:
First, it quantifies the massive scale of climate change impacts on health, projecting 4.1-5.2 billion climate-related disease cases and 14.5-15.6 million deaths in LMICs by 2050. This represents a significant advancement over previous estimates, which the report demonstrates were substantial underestimates.
Second, it illuminates the profound economic consequences, calculating costs of $8.6-20.8 trillion by 2050 (0.7-1.3% of LMIC GDP). The report employs both Value of Statistical Life and Years of Life Lost approaches to provide a range of economic impact estimates.
Third, it reveals stark geographic inequities in impact distribution, with Sub-Saharan Africa bearing approximately 71% of cases and nearly half of deaths, while South Asia faces about 18% of cases and a quarter of deaths. This spatial analysis helps identify where interventions are most urgently needed.
Policy implications and systemic perspectives
The report’s findings point to several critical policy directions:
- The need for systemic rather than disease-specific interventions emerges as a central theme. The authors explicitly advocate for strengthening entire health systems rather than pursuing vertical disease programs.
- The economic analysis makes a compelling case for immediate action, demonstrating that the costs of inaction far exceed potential investment requirements for climate-resilient health systems.
- The geographic distribution of impacts highlights the need for globally coordinated responses while prioritizing support for the most vulnerable regions.
The findings suggest that transforming systems to address climate change impacts on health requires not just technical solutions but fundamental rethinking of how health systems are organized and financed in vulnerable regions.
This aligns with recent scholarship on complex adaptive systems and organizational transformation in global health.
The report’s emphasis on systemic approaches represents a significant shift in thinking about climate-health interventions. This merits unpacking on several levels:
- Inadequacy of vertical disease silos: The report challenges the traditional vertical disease management paradigm that has dominated global health programming for decades. While vertical programs have achieved notable successes in areas like HIV/AIDS or malaria control, the report argues that climate change’s multifaceted health impacts require a fundamentally different approach.
- Need for systemic intervention: Climate change simultaneously affects multiple disease pathways, nutrition status, and health infrastructure. These interactions cannot be effectively addressed through isolated disease-specific programs. Building core health system capabilities (surveillance, emergency response, primary care) creates multiplicative benefits across various climate-related health challenges. Strong health systems can better identify and respond to emerging threats, whereas vertical programs often lack this flexibility.
- Implementation implications: The report suggests this systemic approach requires: integrated planning across health system components, flexible funding mechanisms that support system-wide capabilities, enhanced coordination between different health programmes and investment in cross-cutting infrastructure and capabilities.
What about the health workforce facing impacts of climate change on health?
Between this clear-eyed assessment and effective action lies a critical implementation gap.
Interestingly, the report gives limited explicit attention to the health workforce dimension of climate-health challenges. Yet that is precisely where we need to focus attention, given that:
- Health workers based in communities are first responders to climate-related health emergencies
- Workforce capacity significantly determines a health system’s adaptive capabilities
- Climate change itself affects health worker distribution and effectiveness
Given the report’s emphasis on systemic approaches, the lack of detailed discussion about human resources for health represents a missed opportunity to explore what effective action might look like.
The Geneva Learning Foundation’s network, developed through nearly a decade of research and practice, has led us to identify a path for supporting the health workforce to strengthen preparedness and response in response to climate change impacts on health.
The network already connects over 60,000 health workers. They represent all job roles, rank, and levels of the health system.
One distinguishing feature of this network is its deep integration with existing government health systems. Over half of network participants are government employees, from community health workers to district officers to national planners.
62% of participants work in remote rural areas, 47% serve urban poor populations, and 21% operate in conflict zones.
These are not just statistics: they represent an unprecedented capability to mobilize knowledge and action where it’s most needed.
Since 2023, network participants have been sharing observations, experiences, and insights of climate change impacts on health.
The model connects different levels of health systems:
- Community-based health workers share ground-level observations
- District managers identify emerging patterns
- National planners gauge system-wide implications
- Global partners access real-time insights
When a malaria control officer in Kenya observes changing disease patterns due to altered rainfall, the network enables rapid sharing of this insight with colleagues working on water safety, nutrition, and primary care. These cross-domain connections do not need to be left to chance – they can be enabled through structured peer learning processes that transcend traditional programme, geographic, and hierarchical boundaries
This creates what organizational theorists call “embedded transformation” – where system change emerges through existing structures rather than requiring new ones.
Rather than creating new coordination mechanisms, the network enables:
- Health workers to learn directly from peers in other programs
- Rapid identification of cross-cutting challenges
- Spontaneous formation of problem-solving groups
- Systematic sharing of effective practices
Rather than replacing existing structures, TGLF’s model demonstrates how digital networks can enable health systems to:
- Maintain necessary specialization while fostering crucial connections
- Enable rapid learning and adaptation across programs
- Optimize resource use through enhanced coordination
- Build system-wide resilience through structured peer learning
Such a network enables what complexity theorists call “distributed sensing” that can provide:
- Early warning of emerging threats
- Rapid sharing of local solutions
- System-wide learning from local innovations
- Continuous adaptation to changing conditions
This has led us to posit that investment in such emergent digital networks could enable health systems to maintain necessary specialization while fostering crucial connections across domains.
This is obviously critical to respond to the systems-level complexity of climate change impacts on health.
World Bank findingTGLF model strategic fitScale of impact (4.1-5.2B cases, 14.5-15.6M deaths by 2050)TGLF’s digital network model demonstrates scalability, already connecting over 60,000 health practitioners across 137 countries. More significantly, the model’s effectiveness increases with scale – as more practitioners join, the network’s ability to identify emerging threats and disseminate effective responses improves. Network analysis shows that larger scale enables more diverse inputs and faster adaptation, suggesting this approach could help health systems respond to the massive scale of projected impacts.Economic consequences ($8.6-20.8T by 2050)TGLF’s model offers remarkable cost-effectiveness through its networked learning structure. Rather than requiring massive new investments in parallel systems, it leverages existing health system resources while enabling and accelerating both learning and action. The model demonstrates how digital infrastructure can maximize return on investment – practitioners implement solutions using existing resources, with 82% reporting ability to continue without external support. This suggests potential for significant cost savings while building system resilience.Geographic inequities (71% SSA, 18% SA)TGLF’s network already demonstrates strongest presence precisely where the World Bank identifies greatest need – 70% of participants work in Sub-Saharan Africa and South Asia. This concentration is not coincidental; the model’s digital infrastructure and peer learning approach prove particularly effective in resource-constrained settings. The network enables rapid sharing of context-appropriate solutions between regions facing similar challenges, while maintaining sensitivity to local conditions.Need for systemic interventionThe network transcends traditional program boundaries through what organizational theorists call “structured emergence” – practitioners naturally form cross-program connections based on shared challenges. When a malaria control officer observes changing disease patterns due to climate shifts, the network enables rapid sharing with colleagues in water safety, nutrition, and primary care. This organic integration emerges through peer learning rather than requiring new coordination mechanisms.Urgency of investmentTGLF’s model offers an immediately scalable approach that builds on existing health system capabilities. Rather than waiting years to develop new infrastructure, the network can rapidly expand to connect more practitioners and regions. Evidence shows 7x acceleration in implementation of new approaches compared to conventional means of technical assistance, suggesting potential for rapid, sustainable strengthening of health system resilience.Global coordination needWhile enabling global connection, the network maintains strong local grounding through its emphasis on locally-led action and contextual adaptation. Government health workers comprise over 50% of participants, creating what scholars term “embedded transformation” – change emerging through existing structures rather than imposed from outside. This enables coordinated response while respecting local health system authority.System transformationThe model demonstrates how digital networks can fundamentally transform how health systems operate without requiring complete restructuring. By enabling rapid knowledge flow across traditional boundaries, supporting emergence of new coordination patterns, and fostering system-wide learning, it shows how transformation can emerge through enhanced connection rather than structural overhaul. Analysis reveals development of new capabilities in surveillance, response, and adaptation through networked learning.Reference
Uribe, J.P., Rabie, T., 2024. The Cost of Inaction: Quantifying the Impact of Climate Change on Health in Low- and Middle-Income Countries. The World Bank, Washington, D.C. https://doi.org/10.1596/42419
Image: The Geneva Learning Foundation Collection © 2024
Share this:
#digitalLearning #globalHealth #health #JuanPabloUribe #LMICs #networkedLearning #peerLearning #TamerRabie #TheCostOfInactionQuantifyingTheImpactOfClimateChangeOnHealth #TheGenevaLearningFoundation #WorldBank
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The cost of inaction: Quantifying the impact of climate change on health
This World Bank report ‘The Cost of Inaction: Quantifying the Impact of Climate Change on Health in Low- and Middle-Income Countries’ presents new analysis of climate change impacts on health systems and outcomes in the regions that are bearing the brunt of these impacts.
Key analytical insights to quantify climate change impacts on health
The report makes three contributions to our understanding of climate-health interactions:
First, it quantifies the massive scale of climate change impacts on health, projecting 4.1-5.2 billion climate-related disease cases and 14.5-15.6 million deaths in LMICs by 2050. This represents a significant advancement over previous estimates, which the report demonstrates were substantial underestimates.
Second, it illuminates the profound economic consequences, calculating costs of $8.6-20.8 trillion by 2050 (0.7-1.3% of LMIC GDP). The report employs both Value of Statistical Life and Years of Life Lost approaches to provide a range of economic impact estimates.
Third, it reveals stark geographic inequities in impact distribution, with Sub-Saharan Africa bearing approximately 71% of cases and nearly half of deaths, while South Asia faces about 18% of cases and a quarter of deaths. This spatial analysis helps identify where interventions are most urgently needed.
Policy implications and systemic perspectives
The report’s findings point to several critical policy directions:
- The need for systemic rather than disease-specific interventions emerges as a central theme. The authors explicitly advocate for strengthening entire health systems rather than pursuing vertical disease programs.
- The economic analysis makes a compelling case for immediate action, demonstrating that the costs of inaction far exceed potential investment requirements for climate-resilient health systems.
- The geographic distribution of impacts highlights the need for globally coordinated responses while prioritizing support for the most vulnerable regions.
The findings suggest that transforming systems to address climate change impacts on health requires not just technical solutions but fundamental rethinking of how health systems are organized and financed in vulnerable regions.
This aligns with recent scholarship on complex adaptive systems and organizational transformation in global health.
The report’s emphasis on systemic approaches represents a significant shift in thinking about climate-health interventions. This merits unpacking on several levels:
- Inadequacy of vertical disease silos: The report challenges the traditional vertical disease management paradigm that has dominated global health programming for decades. While vertical programs have achieved notable successes in areas like HIV/AIDS or malaria control, the report argues that climate change’s multifaceted health impacts require a fundamentally different approach.
- Need for systemic intervention: Climate change simultaneously affects multiple disease pathways, nutrition status, and health infrastructure. These interactions cannot be effectively addressed through isolated disease-specific programs. Building core health system capabilities (surveillance, emergency response, primary care) creates multiplicative benefits across various climate-related health challenges. Strong health systems can better identify and respond to emerging threats, whereas vertical programs often lack this flexibility.
- Implementation implications: The report suggests this systemic approach requires: integrated planning across health system components, flexible funding mechanisms that support system-wide capabilities, enhanced coordination between different health programmes and investment in cross-cutting infrastructure and capabilities.
What about the health workforce facing impacts of climate change on health?
Between this clear-eyed assessment and effective action lies a critical implementation gap.
Interestingly, the report gives limited explicit attention to the health workforce dimension of climate-health challenges. Yet that is precisely where we need to focus attention, given that:
- Health workers based in communities are first responders to climate-related health emergencies
- Workforce capacity significantly determines a health system’s adaptive capabilities
- Climate change itself affects health worker distribution and effectiveness
Given the report’s emphasis on systemic approaches, the lack of detailed discussion about human resources for health represents a missed opportunity to explore what effective action might look like.
The Geneva Learning Foundation’s network, developed through nearly a decade of research and practice, has led us to identify a path for supporting the health workforce to strengthen preparedness and response in response to climate change impacts on health.
The network already connects over 60,000 health workers. They represent all job roles, rank, and levels of the health system.
One distinguishing feature of this network is its deep integration with existing government health systems. Over half of network participants are government employees, from community health workers to district officers to national planners.
62% of participants work in remote rural areas, 47% serve urban poor populations, and 21% operate in conflict zones.
These are not just statistics: they represent an unprecedented capability to mobilize knowledge and action where it’s most needed.
Since 2023, network participants have been sharing observations, experiences, and insights of climate change impacts on health.
The model connects different levels of health systems:
- Community-based health workers share ground-level observations
- District managers identify emerging patterns
- National planners gauge system-wide implications
- Global partners access real-time insights
When a malaria control officer in Kenya observes changing disease patterns due to altered rainfall, the network enables rapid sharing of this insight with colleagues working on water safety, nutrition, and primary care. These cross-domain connections do not need to be left to chance – they can be enabled through structured peer learning processes that transcend traditional programme, geographic, and hierarchical boundaries
This creates what organizational theorists call “embedded transformation” – where system change emerges through existing structures rather than requiring new ones.
Rather than creating new coordination mechanisms, the network enables:
- Health workers to learn directly from peers in other programs
- Rapid identification of cross-cutting challenges
- Spontaneous formation of problem-solving groups
- Systematic sharing of effective practices
Rather than replacing existing structures, TGLF’s model demonstrates how digital networks can enable health systems to:
- Maintain necessary specialization while fostering crucial connections
- Enable rapid learning and adaptation across programs
- Optimize resource use through enhanced coordination
- Build system-wide resilience through structured peer learning
Such a network enables what complexity theorists call “distributed sensing” that can provide:
- Early warning of emerging threats
- Rapid sharing of local solutions
- System-wide learning from local innovations
- Continuous adaptation to changing conditions
This has led us to posit that investment in such emergent digital networks could enable health systems to maintain necessary specialization while fostering crucial connections across domains.
This is obviously critical to respond to the systems-level complexity of climate change impacts on health.
World Bank findingTGLF model strategic fitScale of impact (4.1-5.2B cases, 14.5-15.6M deaths by 2050)TGLF’s digital network model demonstrates scalability, already connecting over 60,000 health practitioners across 137 countries. More significantly, the model’s effectiveness increases with scale – as more practitioners join, the network’s ability to identify emerging threats and disseminate effective responses improves. Network analysis shows that larger scale enables more diverse inputs and faster adaptation, suggesting this approach could help health systems respond to the massive scale of projected impacts.Economic consequences ($8.6-20.8T by 2050)TGLF’s model offers remarkable cost-effectiveness through its networked learning structure. Rather than requiring massive new investments in parallel systems, it leverages existing health system resources while enabling and accelerating both learning and action. The model demonstrates how digital infrastructure can maximize return on investment – practitioners implement solutions using existing resources, with 82% reporting ability to continue without external support. This suggests potential for significant cost savings while building system resilience.Geographic inequities (71% SSA, 18% SA)TGLF’s network already demonstrates strongest presence precisely where the World Bank identifies greatest need – 70% of participants work in Sub-Saharan Africa and South Asia. This concentration is not coincidental; the model’s digital infrastructure and peer learning approach prove particularly effective in resource-constrained settings. The network enables rapid sharing of context-appropriate solutions between regions facing similar challenges, while maintaining sensitivity to local conditions.Need for systemic interventionThe network transcends traditional program boundaries through what organizational theorists call “structured emergence” – practitioners naturally form cross-program connections based on shared challenges. When a malaria control officer observes changing disease patterns due to climate shifts, the network enables rapid sharing with colleagues in water safety, nutrition, and primary care. This organic integration emerges through peer learning rather than requiring new coordination mechanisms.Urgency of investmentTGLF’s model offers an immediately scalable approach that builds on existing health system capabilities. Rather than waiting years to develop new infrastructure, the network can rapidly expand to connect more practitioners and regions. Evidence shows 7x acceleration in implementation of new approaches compared to conventional means of technical assistance, suggesting potential for rapid, sustainable strengthening of health system resilience.Global coordination needWhile enabling global connection, the network maintains strong local grounding through its emphasis on locally-led action and contextual adaptation. Government health workers comprise over 50% of participants, creating what scholars term “embedded transformation” – change emerging through existing structures rather than imposed from outside. This enables coordinated response while respecting local health system authority.System transformationThe model demonstrates how digital networks can fundamentally transform how health systems operate without requiring complete restructuring. By enabling rapid knowledge flow across traditional boundaries, supporting emergence of new coordination patterns, and fostering system-wide learning, it shows how transformation can emerge through enhanced connection rather than structural overhaul. Analysis reveals development of new capabilities in surveillance, response, and adaptation through networked learning.Reference
Uribe, J.P., Rabie, T., 2024. The Cost of Inaction: Quantifying the Impact of Climate Change on Health in Low- and Middle-Income Countries. The World Bank, Washington, D.C. https://doi.org/10.1596/42419
Image: The Geneva Learning Foundation Collection © 2024
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Knowing-in-action: Bridging the theory-practice divide in global health
The gap between theoretical knowledge and practical implementation remains one of the most persistent challenges in global health. This divide manifests in multiple ways: research that fails to address practitioners’ urgent needs, innovations from the field that never inform formal evidence systems, and capacity building approaches that cannot meet the massive scale of learning required. Donald Schön’s seminal 1995 analysis of the “dilemma of rigor or relevance” in professional practice offers crucial insights for “knowing-in-action“.
Schön’s analysis: The dilemma of rigor or relevance
Schön begins by examining how knowledge becomes institutionalized through education. Using elementary school mathematics as an example, he describes how knowledge is broken into discrete units (“math facts”), organized into progressive modules, assembled into curricula, and measured through standardized tests. This systematization shapes not just content but the entire organization of time, space, and institutional arrangements.
From this foundation, Schön introduces his central metaphor of two contrasting landscapes in professional practice that prevent “knowing-in-action”. As he describes it:
“In the varied topography of professional practice, there is a high, hard ground overlooking a swamp. On the high ground, manageable problems lend themselves to solution through the use of research-based theory and technique. In the swampy lowlands, problems are messy and confusing and incapable of technical solution.”
The cruel irony, Schön observes, lies in the relative importance of these terrains: “The problems of the high ground tend to be relatively unimportant to individuals or to society at large, however great their technical interest may be, while in the swamp lie the problems of greatest human concern.”
This creates what Schön calls the “dilemma of rigor or relevance” – practitioners must choose between remaining on the high ground where they can maintain technical rigor or descending into the swamp where they must rely on experience, intuition, and what he terms “muddling through.”
The historical roots of the divide
Schön traces this dilemma to the epistemology embedded in modern research universities. Drawing on Edward Shils’s historical analysis, he describes how American scholars returning from Germany after the Civil War brought back “the German idea of the university as a place in which to do research that contributes to fundamental knowledge, preferably through science.”
This was, as Schön notes, “a very strange idea in 1870,” running counter to the prevailing British model of universities as sanctuaries for liberal arts or finishing schools for gentlemen. The new model first took root at Johns Hopkins University, whose president embraced the “bizarre notion that professors should be recruited, promoted, and granted tenure on the basis of their contributions to fundamental knowledge.”
This shift created what Schön terms the “Veblenian bargain” (named after Thorstein Veblen), establishing a separation between:
- Research universities focused on “true scholarship” and fundamental knowledge
- Professional schools dedicated to practical training
Knowing-in-action in global health: From fragmentation to integration
The historical division between theory and practice that Schön identified continues to shape global health in profound and often problematic ways. This manifests in three interconnected challenges that demand our urgent attention: the knowledge-practice gap, the scale challenge, and the complexity challenge. Yet emerging approaches suggest potential paths forward, particularly through structured peer learning networks that could help bridge Schön’s “high ground” and “swamp.”
Three fundamental challenges
Challenge #1: The knowing-in-action divide
The separation between research institutions and field practice creates not just an academic concern but a practical crisis in healthcare delivery. Consider the response to COVID-19: while research institutions rapidly generated new knowledge about the virus, frontline health workers struggled to translate this into practical approaches for their specific contexts. Their hard-won insights about what worked in different settings rarely made it back into formal evidence systems, epitomizing the one-way flow of knowledge that impoverishes both research and practice.
This pattern repeats across global health. Research agendas, shaped by academic incentives and funding priorities, often fail to address practitioners’ most pressing challenges. A community health worker in rural Bangladesh facing complex challenges around vaccine hesitancy may struggle to find relevant guidance – while global experts are convinced that they already have all the answers. Meanwhile, local solutions to building vaccine confidence remain uncaptured by formal knowledge systems.
The rise of implementation science attempts to bridge this divide, yet often remains subordinate to “pure” research in academic hierarchies. This reflects Schön’s observation about the privileging of high ground problems over swampy ones, even when the latter hold greater practical significance.
Challenge #2: The scale imperative
Traditional approaches to professional education face fundamental limitations in meeting the massive need for health worker capacity building. The World Health Organization projects a shortfall of 10 million health workers by 2030, mostly in low- and middle-income countries. Conventional training approaches that rely on cascading knowledge through workshops and formal courses can reach only a fraction of those who need support.
More fundamentally, these knowledge transmission models prove inadequate for addressing complex local realities. A standardized curriculum developed by experts, no matter how well-designed, cannot anticipate the diverse challenges health workers face across different contexts. When a district immunization manager in Nigeria must adapt vaccination strategies for nomadic populations during a drought, they need more than pre-packaged knowledge – they need ways to learn from others who are facing similar challenges.
Resource constraints further limit the reach of conventional approaches. The cost of traditional training programmes, both in money and time away from service delivery, makes it impossible to scale them to meet the need. Yet the human cost of this capacity gap, measured in preventable illness and death, demands urgent solutions.
Challenge #3: The complexity conundrum
Contemporary global health faces challenges that fundamentally resist standardized technical solutions. Climate change exemplifies this complexity, creating cascading effects on health systems and communities that cannot be addressed through linear interventions. When rising temperatures alter disease patterns while simultaneously disrupting cold chains for vaccine delivery, no single technical fix suffices.
Similarly, emerging and re-emerging infectious diseases demand responses that cross traditional boundaries between animal and human health, environmental factors, and social determinants. Health workforce development must grapple with complex systemic issues around motivation, retention, and capacity building. The COVID-19 pandemic demonstrated how traditional approaches to health system strengthening often prove inadequate in the face of complex adaptive challenges.
Emerging solutions: A new paradigm for learning and practice
Recent innovations suggest promising approaches to bridging these divides through structured peer learning networks. Digital platforms enable health workers to share experiences and solutions across geographical boundaries, creating new possibilities for scaled learning that maintains local relevance.
Solution #1: The power of structured peer learning
Experience from digital learning networks demonstrates how structured peer interaction can enable more efficient and effective knowledge sharing than traditional top-down approaches. When health workers can directly connect with peers facing similar challenges, they not only share solutions but collectively generate new knowledge through their interactions.
These networks provide mechanisms for validating practical knowledge through peer review processes that complement traditional academic validation. A successful intervention developed by a rural clinic in Thailand can be critically examined by peers, adapted for different contexts, and rapidly disseminated across the network. This creates a more dynamic and responsive knowledge ecosystem than traditional publication cycles allow.
Solution #2: Network effects and collective intelligence
The potential of practitioner networks extends beyond simple knowledge sharing. When properly structured, these networks create possibilities for:
- Rapid adaptation to emerging challenges through real-time sharing of experiences
- Collective problem-solving that draws on diverse perspectives and contexts
- Systematic capture and analysis of field innovations
- Development of context-specific solutions that build on shared learning
Most importantly, these networks can help bridge Schön’s high ground and swamp by creating dialogue between different forms of knowledge and practice. They provide spaces where academic research can inform field practice while simultaneously allowing field insights to shape research agendas.
Four principles toward knowing-in-action for global health
Drawing on Schön’s call for a “new epistemology,” we can identify four principles for transforming how we know what we know in global health:
Principle #1: Valuing multiple forms of knowledge
The complexity of contemporary health challenges demands recognition of multiple valid forms of knowledge. The practical wisdom developed by a community health worker through years of service deserves attention alongside randomized controlled trials. This requires challenging existing hierarchies of evidence while maintaining rigorous standards for validating knowledge claims.
Principle #2: Enabling knowledge creation from practice
Health workers must be supported as knowledge producers, not just knowledge consumers. This means creating structures for systematically capturing and validating field insights, building evidence from implementation experience, and enabling continuous learning from practice. Digital platforms can provide scaffolding for this knowledge creation while ensuring quality through peer review processes.
Principle #3: Scaling through networked learning
Traditional scaling approaches that rely on standardization and top-down dissemination must be complemented by networked learning to create and amplify knowing-in-action. This means building systems that can:
- Connect practitioners across contexts and boundaries
- Enable peer validation of knowledge
- Support rapid dissemination of innovations
- Build collective intelligence through structured interaction
Principle #4: Embracing complexity
Rather than seeking to reduce complexity through standardization, health systems must build capacity for working effectively within complex adaptive systems. This means supporting adaptive learning, enabling context-specific solutions, and building capacity for systems thinking at all levels.
The challenges facing global health today demand new ways of creating, validating, and sharing knowledge. By embracing approaches that bridge Schön’s high ground and swamp, we may find paths toward health systems that are both more rigorous and more relevant to the communities they serve.
Looking forward
Schön’s analysis helps explain why traditional approaches to global health knowledge and learning often fall short. More importantly, it points toward solutions that could help bridge the theory-practice divide to support knowing-in-action:
- New digital platforms that enable peer learning at scale
- Networks that connect practitioners across contexts
- Approaches that validate practical knowledge
- Systems that support rapid learning and adaptation
Schön’s insights remain remarkably relevant to contemporary global health challenges. His call for a new epistemology that can bridge theory and practice speaks directly to our current needs. By embracing new approaches to learning and knowledge creation that honor both rigor and relevance, we may find ways to address the complex challenges that lie ahead.
The key lies not in choosing between high ground and swamp, but in building new kinds of bridges between them – bridges that can support the massive scale of learning needed while maintaining the local relevance essential for impact. Recent innovations in peer learning networks and digital platforms suggest this bridging may be increasingly possible, offering hope for more effective global health practice in an increasingly complex world.
The challenge now is to develop and implement these bridging approaches at the scale needed to support global health workers worldwide. This will require new ways of thinking about knowledge, learning, and practice – ways that honor both the rigor of research and the wisdom of experience. The future of global health may depend on our success in this endeavor.
Reference
Schön, Donald A., 1995. Knowing-in-action: The new scholarship requires a new epistemology. Change: The Magazine of Higher Learning 27, 27–34. https://doi.org/10.1080/00091383.1995.10544673
Image: The Geneva Learning Foundation Collection © 2024
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