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Tag: climate and health

  • WHO Global Conference on Climate and Health: New pathways to overcome structural barriers blocking effective climate and health action

    WHO Global Conference on Climate and Health: New pathways to overcome structural barriers blocking effective climate and health action

    After the World Health Assembly’s adoption of ambitious global plan of action for climate and health, global and country stakeholders are meeting in Brasilia for the Global Conference on Climate and Health, ahead of COP30. Three critical observations emerged that illuminate why conventional global health approaches may be structurally inadequate for the challenges resulting from climate change impacts on health.

    These observations carry particular significance for global health leaders who now possess a WHA-approved strategy and action plan, but lack proven mechanisms for rapid, community-led implementation in the face of an unprecedented set of challenges. They also matter for major funders whose substantial investments in policy and research have yet to be matched by commensurate support for the communities and health workers who will be the ones to translate better science and policy into action.

    Signal 1: When funding disappears and demand explodes

    Seventy percent of global health funding vanished, virtually overnight. This collapse comes precisely when the World Health Organization projects a shortage of 10 million health workers by 2030—six million in climate-vulnerable sub-Saharan Africa.

    The World Bank calculates that climate change will generate 4.1-5.2 billion disease cases and cost $8.6-20.8 trillion by 2050 in low- and middle-income countries alone. Health systems must simultaneously manage unprecedented demand with drastically reduced resources.

    Traditional technical assistance—flying experts to conduct workshops, cascade training through hierarchies—is more difficult to resource. By comparison, peer learning networks can reduce costs by 86 percent while achieving implementation rates seven times higher than conventional methods. Furthermore, 82 percent of participants in such networks continue independently after formal interventions end. Peer learning is especially well-suited to include health workers in conflict zones, refugee settings, and remote areas where climate vulnerability peaks—precisely the locations where traditional expert-led capacity building proves most difficult and expensive.

    The funding crisis makes it more of an imperative than ever before to examine which approaches can scale effectively when resources contract. Organizations that recognize this shift early could achieve breakthrough results as traditional approaches become unaffordable.

    Signal 2: Global expertise meets local reality

    The World Health Assembly continues producing comprehensive action plans backed by thousands of expert hours. The climate and health action plan represents the pinnacle of this approach—technically excellent, evidence-based, globally applicable.

    Yet the persistent implementation gap reflects deeper challenges about how knowledge flows between institutions and communities. Current theories of change assume that technical expertise, properly communicated, will lead to improved outcomes. Local knowledge gets framed as “barriers to implementation”, rather than recognized as essential intelligence for adaptation.

    This creates a paradox. The WHO recognizes that “community-led initiatives that harness local knowledge and practices” are “fundamental for creating interventions that are both culturally appropriate and effective.” Health workers possess sophisticated understanding of how global frameworks must adapt to local realities. But systematic mechanisms for capturing and integrating knowledge and action remain underdeveloped.

    Climate change manifests differently in each community—shifting disease patterns in Kenya differ from changing agricultural cycles in Bangladesh, which differ from altered water availability in Morocco. Health workers witness these changes daily, developing contextual responses that often remain invisible to global institutions. The question becomes whether global frameworks can evolve to recognize and systematically integrate this distributed intelligence rather than treating it as anecdotal evidence.

    Signal 3: The policy-people gap widens if field-building ignores communities and is disconnected from local action

    Substantial philanthropic funding is flowing toward climate and health policy and evidence generation. Some funders call this “field-building”. Research institutions develop sophisticated models. Policy frameworks become more comprehensive. Scientific understanding advances rapidly. These investments are producing genuinely better science and more effective policies—essential progress that must continue.

    Yet investment in communities and health workers—the people who must implement policies and apply evidence—remains disproportionately small. This disparity creates concerning dynamics where knowledge advances faster than the capacity to apply it meaningfully in communities.

    The risk extends beyond implementation gaps. When sophisticated policies and evidence develop without commensurate investment in community relationships, communities may reject even superior science and policies—not because they are irrational or too ignorant to recognize the benefits, but because the effort to accompany communities through change has been insufficient. Health workers, as trusted advisors within their communities, are uniquely positioned to bridge this gap by helping communities make sense of new evidence and adapt policies to local realities.

    Health workers serve as trusted advisors within communities facing climate impacts. When investment patterns overlook this relationship, sophisticated policies risk becoming irrelevant to the people they aim to help. The trust networks essential for translating evidence into community action – and ensuring that evidence is relevant and useful – receive less attention than the evidence itself.

    The pathway forward: Health workers as knowledge creators and leaders of change

    These three signals point toward a fundamental misalignment between how global institutions approach climate and health challenges and how communities experience them. The funding crisis makes traditional expert-led approaches unsustainable. Implementation gaps persist because local knowledge remains systematically undervalued. Investment patterns favor sophisticated frameworks over the human relationships needed to apply them effectively.

    When a community health worker in Nigeria notices malaria cases appearing earlier each season, or a nurse in Bangladesh observes heat-related illness patterns in specific neighborhoods, they are detecting signals that epidemiological studies might take years to document formally. This represents a form of “early warning system” that current approaches tend to overlook.

    Recent innovations demonstrate different possibilities. Networks connecting health practitioners across countries through digital platforms treat health workers as knowledge creators rather than knowledge recipients. Such approaches have achieved, in other fields, implementation rates seven times higher than conventional technical assistance while reducing costs by 86 percent. There is no reason why applying these approaches would not result in similar results. 

    For the World Health Organization, such approaches could offer pathways to operationalize the Global Plan of Action through the very health workers the organization recognizes as “uniquely positioned” to champion climate action while building essential community trust.

    For major funders, these models represent opportunities to complement policy and research investments with approaches that strengthen community capacity to apply sophisticated knowledge to local realities.

    The evidence suggests that failure to bridge these gaps could prove more costly than the investment required to close them. But the returns—measured in communities reached, knowledge applied, and trust maintained—justify treating health worker networks as essential infrastructure for climate and health response rather than optional additions.

    Three questions for leaders

    As leaders prepare for the Global Climate Change and Health conference in Brasilia and begin work to implement climate and health commitments, three questions emerge from the World Health Assembly observations:

    • For institutions with comprehensive plans: How will technical excellence translate into community-level implementation when traditional capacity building approaches have become economically unsustainable?
    • For funders investing in research and policy: How can sophisticated evidence and frameworks reach the health workers and communities who must apply them to local realities?
    • For all climate and health leaders: What happens when policies advance faster than the trust relationships and implementation capacity needed to apply them effectively?

    The signals from the World Health Assembly suggest that conventional approaches face structural constraints that incremental improvements cannot address. The funding crisis, implementation gaps, and investment disparities require responses that recognize health workers as partners in creating climate and health solutions rather than merely implementing plans created elsewhere.

    The choice is not whether to transform approaches—resource constraints and community realities make transformation inevitable. The choice is whether leaders will direct that transformation toward approaches that strengthen both global knowledge and local capacity, or risk watching sophisticated frameworks fail for lack of community connection and trust.

    References

    Miller, J., Howard, C., Alqodmani, L., 2024. Advocating for a Healthy Response to Climate Change — COP28 and the Health Community. N Engl J Med 390, 1354–1356. https://doi.org/10.1056/NEJMp2314835

    Sanchez, J.J., Gitau, E., Sadki, R., Mbuh, C., Silver, K., Berry, P., Bhutta, Z., Bogard, K., Collman, G., Dey, S., Dinku, T., Dwipayanti, N.M.U., Ebi, K., Felts La Roca Soares, M., Gudoshava, M., Hashizume, M., Lichtveld, M., Lowe, R., Mateen, B., Muchangi, M., Ndiaye, O., Omay, P., Pinheiro Dos Santos, W., Ruiz-Carrascal, D., Shumake-Guillemot, J., Stewart-Ibarra, A., Tiwari, S., 2025. The climate crisis and human health: identifying grand challenges through participatory research. The Lancet Global Health. https://doi.org/10.1016/S2214-109X(25)00003-8

    Sadki, R., 2024. Health at COP29: Workforce crisis meets climate crisis. https://doi.org/10.59350/sdmgt-ptt98

    Sadki, R., 2024. Strengthening primary health care in a changing climate. https://doi.org/10.59350/5s2zf-s6879

    Sadki, R., 2024. The cost of inaction: Quantifying the impact of climate change on health. https://doi.org/10.59350/gn95w-jpt34

    Image: The Geneva Learning Foundation Collection © 2025

  • Why peer learning is critical to survive the Age of Artificial Intelligence

    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 networks
    Powerful tools but limited adaptation
    Insights remain isolated
    Success depends on individual skill    		Continuous adaptation through structured sharing
    Insights multiply across network through peer review
    Collective wisdom enhances individual capability
    AI recommendations may miss local context
    Trial and error in isolation
    Slow spread of effective practices    		Context-aware applications emerge through dialogue
    Structured experimentation with collective learning
    Rapid diffusion through documented innovations
    Overwhelmed by rapid AI changes
    Struggling to keep pace alone
    Uncertainty about appropriate use    		Collective sense-making through facilitated sessions
    Shared discovery in peer projects
    Growing confidence through structured support

    The 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

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  • The great technical assistance disruption: How peer networks outperform experts at a fraction of the cost

    The great technical assistance disruption: How peer networks outperform experts at a fraction of the cost

    “If health workers do not share their challenges and solutions, we are bound to fail.” This declaration from a participant in the Teach to Reach initiative facilitated by The Geneva Learning Foundation (TGLF) cuts to the heart of a crisis that has long plagued global health technical assistance: the persistent gap between what external experts provide and what practitioners actually need.

    At the annual meeting of the American Society of Tropical Medicine and Hygiene (ASTMH), TGLF’s Reda Sadki presented evidence of a quiet revolution taking place in how global health organizations approach capacity building and technical assistance. His research and practice demonstrate that digitally-enabled peer learning can overcome fundamental limitations that have constrained traditional models for decades. The implications challenge not just how we train health workers, but the entire infrastructure of expert-driven technical assistance that dominates global health.

    Why we resist learning from screens

    To understand why this revolution has been so long in coming, Sadki traced our resistance to digital learning back to philosophical roots that run deeper than most global health practitioners realize. The skepticism, he argued, stems from a fundamental assumption about how real learning occurs — an assumption that shapes everything from how we design training programs to how we structure technical assistance.

    “Plato initiated our traditional negative view of the written word,” Sadki explained, describing how the ancient philosopher believed that writing “detaches the message from its author and transforms it into a dead thing, a text.” For Plato, authentic learning required direct interaction between teacher and student. Anything mediated — whether by writing or, by extension, digital technology — was considered a pale imitation of real knowledge transfer.

    This ancient skepticism persists in modern global health, where the dominant assumption is that learning means recalling information and teaching means transmitting that information through direct instruction. Face-to-face workshops and expert-led training sessions are considered “real” technical assistance, while digital alternatives are viewed as convenient but inferior substitutes.

    “It is a false dichotomy to distinguish between, to oppose our lived reality to the digital one,” Sadki argued. “The digital one is lived also. It is also reality.” Yet this dichotomy continues to shape technical assistance models that prioritize flying experts around the world to deliver content in person, even when evidence suggests digital approaches may be more effective.

    Indeed, the evidence is striking. Two major meta-analyses comparing learning modalities found that “distance learning results have been consistently better” than traditional face-to-face approaches, “and that has been the case since 1991.” Yet global health technical assistance remains largely wedded to what Bill Cope and Mary Kalantzis call a “didactic learning architecture” — the familiar setup where external experts deliver content to passive recipients arranged “in rows, they do not speak to each other, the teacher sits at the front.”

    When information transmission fails

    The inadequacy of information transmission models becomes clear when considering the nature of challenges that health workers actually face. Most global health training assumes that the problem is a lack of information — that if practitioners simply knew more facts or protocols, they would perform better. This assumption drives technical assistance focused on delivering standardized content through lectures, presentations, and workshops.

    But research in learning science reveals a more complex reality. “When knowledge is a river, not a reservoir, process, not a product,” expert-led information transmission breaks down, Sadki observed. Modern knowledge workers have “around 10 percent” of the knowledge they need “right there in your brain,” with “90 percent of what you need to know going to come from other humans, or increasingly from machines.”

    This insight challenges the foundation of traditional technical assistance. If practitioners need to access knowledge through connections rather than storage, then the goal should not be filling their heads with information but connecting them to networks where knowledge flows. Yet most capacity building programs continue to focus on what Sadki called “content-driven learning” rather than connection-driven learning.

    The shift required is profound. Rather than positioning external experts as the primary source of knowledge, effective technical assistance must create what Connell Foley described as “a fundamental shift from being an expert who provides answers, to being a facilitator who, through critical thought, can develop questions that prompt others to analyze and develop strategies to address their own needs.”

    Digital technologies as technical assistance disruptors

    The breakthrough comes when digital technologies “enable you to defy distance and boundaries in order to connect with others and learn from them.” This represents more than technological innovation — it challenges the basic economics and power structures of traditional technical assistance.

    Consider the conventional model: international organizations identify capacity gaps, hire external experts, and deploy them to deliver training. This approach assumes that valid knowledge flows primarily from international experts to local practitioners. It requires significant funding for travel, venues, and expert fees, limiting both reach and frequency of interaction.

    Digitally-enabled peer learning turns this model on its head. “Peer learning has always been there,” Sadki noted. “Learning from others, learning from people who are like yourself has always been important, but it has been limited to those within your physical space.” Digital technologies remove that spatial limitation, enabling practitioners facing similar challenges across different contexts to learn directly from each other.

    Cristina Guerrero, an emergency health doctor who leads a helicopter rescue team in Cadiz, Spain, experienced this transformation through the foundation’s #Ambulance! programme with the International Federation of Red Cross and Red Crescent Societies (IFRC) and the International Committee of the Red Cross (ICRC). “I thought I already knew how to face violence,” she reflected. “Then I heard how they do things in other parts of the world. I learned how I can do my work differently. I became mindful in new ways.”

    Her experience illustrates what traditional technical assistance models struggle to achieve: not just information transfer, but genuine transformation of practice. Sadki noted that peer learning produced “changes in mindfulness” — higher-order learning that most would consider “impossible to achieve by digital means.” Yet “digital combined with social and peer learning made it possible.”

    Evidence of a new technical assistance model

    TGLF’s collaboration with the World Health Organization, implementing 46 cohorts of peer learning initiatives focused on immunization and other technical areas, provided rigorous evidence that peer learning can replace traditional expert-led technical assistance. The first impact evaluation of this collaboration in January 2019 found that “these are more than just courses. These are interventions designed to foster and improve practice at every level.”

    This approach represents what researcher Alexandra Nastase and colleagues would recognize as a fourth model of technical assistance, beyond their three categories of capacity substitution, supplementation, and development. This model challenges fundamental assumptions about who holds valid knowledge and how capacity building should occur.

    The most dramatic validation came through TGLF’s Impact Accelerator mechanism. When 644 alumni signed a pledge to achieve impact in July 2019, something remarkable happened. “‘We are together’ became a slogan for the individuals involved,” Sadki observed. The measurable results were astonishing: participants who engaged in peer learning showed seven times higher rates of project implementation compared to a control group that did not engage in peer learning activities to support and learn from each other.

    The scale of subsequent initiatives has been even more striking. The Movement for Immunization Agenda 2030, launched in March 2022, grew to 6,185 participants in its first two weeks. In the first four months, more than 1,000 developed action plans, and over 4,000 joined a new Impact Accelerator. Within this period, 30 percent of participants reported successful implementation of their local projects — implementation rates that far exceed what traditional technical assistance typically achieves.

    Beyond the expert monopoly

    Perhaps most significantly, the Geneva Learning Foundation’s model has enabled practitioners to transcend traditional power structures and drive their own capacity building agendas. Rather than waiting for external technical assistance, practitioners began forming organic learning networks that generate solutions from the ground up.

    These examples illustrate a fundamental shift in the locus of knowledge creation. Traditional technical assistance assumes that solutions flow from international experts to local implementers. The foundation’s model demonstrates that practitioners facing similar challenges often hold the keys to solutions, and that the role of technical assistance should be creating conditions for them to learn from each other.

    Transforming the technical assistance paradigm

    The evidence points toward what Sadki called “an opportunity for transformation that may be much harder to achieve [than what we already know how to do], but with a far greater return on the investment.” The transformation involves “empowering health professionals to drive improvement from the ground up, connecting them to their peers, and linking to global guidance.”

    This requires fundamentally different approaches to capacity building. Instead of the traditional model where external experts deliver knowledge to passive recipients, effective peer learning creates what Sadki described as “circular, interactive configurations” where practitioners engage directly with each other’s experiences. The facilitation may be digital, but the knowledge exchange is profoundly collaborative.

    By systematically applying insights from social learning, networked learning, and digital learning, the foundation has created what amounts to “a human knowledge network” that “unites practitioners and those who support them in a shared pledge to turn knowledge into action.”

    The fact that these “recent advances in learning science remain largely unknown in global health, at least in some quarters” remains a challenge.

    The future of technical assistance

    As global health faces increasingly complex challenges — from climate change to pandemic preparedness to health system resilience — the ability to harness collective intelligence through peer learning may prove essential. The evidence suggests that effective solutions emerge not from more sophisticated expert-driven interventions, but from better systems for enabling practitioners to learn from each other.

    The implications extend beyond individual capacity building to systemic change. When health workers share challenges and solutions across contexts, they create what Sadki called “a river of knowledge” that practitioners can dip into when they need to solve a problem. This enables rapid adaptation and innovation at scales that traditional technical assistance cannot achieve.

    The revolution in global health technical assistance may ultimately be less about technology and more about recognition — acknowledging that expertise is distributed rather than concentrated, and that the future lies not in perfecting systems for delivering knowledge from experts to practitioners, but in creating conditions for practitioners to take action by combining what they know because they are there every day with the best available global knowledge – reshaping global knowledge in the process.

    References

    Feenberg, A., 1989. The written world: On the theory and practice of computer conferencing, in: Mason, R., Kaye, A. (Eds.), Mindweave: Communication, Computers, and Distance Education. Pergamon Press, pp. 22–39.

    Foley, C., 2008. Developing critical thinking in NGO field staff. Development in Practice 18, 774–778. https://doi.org/10.1080/09614520802386827

    Jurgenson, N., 2012. The IRL Fetish. The New Inquiry 6.

    Kalantzis M, Cope B. Didactic literacy pedagogy. In: Literacies. Cambridge University Press; 2012:63-94.

    Means, B., Toyama, Y., Murphy, R., Bakia, M., Jones, K., 2010. Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. U.S. Department of Education  Office of Planning, Evaluation, and Policy Development  Policy and Program Studies Service.

    Nastase, A., Rajan, A., French, B., Bhattacharya, D., 2020. Towards reimagined technical assistance: the current policy options and opportunities for change. Gates Open Res 4, 180. https://doi.org/10.12688/gatesopenres.13204.1

    Neumann, Y., Shachar, M., 2010. Twenty Years of Research on the Academic Performance Differences Between Traditional and Distance Learning: Summative Meta-Analysis and Trend Examination. MERLOT Journal of Online Learning and Teaching 6.

    Sadki, R. (2022). Learning for Knowledge Creation: The WHO Scholar Program. Reda Sadki. https://doi.org/10.59350/j4ptf-x6x22

    Sadki, R. (2023). Learning-based complex work: how to reframe learning and development. Reda Sadki. https://doi.org/10.59350/7fe95-1fz14

    Sadki, R. (2024). Knowing-in-action: Bridging the theory-practice divide in global health. Reda Sadki. https://doi.org/10.59350/4evj5-vm802

    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

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  • The funding crisis solution hiding in plain sight

    The funding crisis solution hiding in plain sight

    “I did not realize how much I could do with what we already have.”

    A Nigerian health worker’s revelation captures what may be the most significant breakthrough in global health implementation during the current funding crisis. While organizations worldwide slash programs and lay off staff, a small Swiss non-profit, The Geneva Learning Foundation (TGLF), is demonstrating how to achieve seven times greater likelihood of improved health outcomes while cutting costs by 90 percent.

    The secret lies not in new technology or additional resources, but in something deceptively simple: health workers learning from and supporting each other.

    Nigeria: Two weeks to connect thousands, four weeks to change, and six weeks to outcomes

    On June 26, 2025, representatives from 153 global health and humanitarian organizations gathered for a closed-door briefing seeking proven solutions to implementation challenges they knew all too well. TGLF presented evidence from the Nigeria Immunization Agenda 2030 Collaborative that sounds almost too good be true to senior leaders who have to make difficult decisions given the funding cuts: documented results at unprecedented speed and scale – and at lower cost.

    Working with Gavi, Nigeria’s Primary Health Care Development Agency, and UNICEF, they facilitated connections among 4,300 health workers and more than 600 local organizations across all Nigerian states, in just two weeks. Not fleeting digital clicks, but what Executive Director Reda Sadki calls “deep, meaningful engagement, sharing of experience, problem solving together.”

    The challenge was reaching zero-dose children in fragile areas affected by armed conflict. The timeline was impossible by traditional standards. The results transformed many skeptics into advocates – including those who initially said it sounded too good to be true.

    A civil society organization (CSO) volunteer reported that government staff initially dismissed the initiative: “They heard about this, thought it was just another CSO initiative. Two weeks in, they came back asking how to join.”

    Funding crisis: How does sharing experience lead to better outcomes?

    What happened next addresses the most critical question about peer learning approaches: do health workers learning from each other actually improve health outcomes?

    TGLF’s comparative research demonstrated that groups using structured peer learning are seven times more likely to achieve measurable health improvements versus conventional approaches.

    In Nigeria, health workers learned the “five whys” root cause analysis from each other. Many said no one had ever asked them: “What do you think we should do?” or “Why do you think that is?” The transformation was both rapid and measurable.

    For example, at the program start, only 25 percent knew their basic health indicators for local areas. “I collect these numbers and pass them on, but I never realized I could use them in my work,” participants reported.

    Four weeks in, they had produced 409 root cause analyses. Many realized that their existing activities were missing these root causes. After six weeks, health workers began credibly reporting attribution of new activities that led to finding and vaccinating zero-dose children.

    Given limited budget, TGLF had to halt development. But here is the key point: more than half of participating have maintained and continued the peer support network independently, addressing sustainability concerns that plague traditional capacity-building efforts.

    The snowball effect at scale

    The breakthrough emerged from what Sadki describes as reaching “critical mass” where motivated participants pull others along. “This requires clearing the rubble of all the legacy of top-down command and control systems, figure out how to negotiate hierarchies, especially because government integration is systematically our goal.”

    Nigeria represents one of four large-scale implementations demonstrating consistent results. In Côte d’Ivoire, 501 health workers from 96 districts mapped out 3.5 million additional vaccinations in four weeks. Global initiatives are likely to cost no more than a single country-specific program: the global Teach to Reach network has engaged 24,610 participants across more than 60 countries. The global Movement for Immunization Agenda 2030, launched in March 2022, grew from 6,186 to more than 15,000 members in less than four months.

    The foundation tracks what they call a “complete measurement chain” from individual motivation through implementation actions to health outcomes. Cost efficiency stems from scale and sustainability, with back-of-envelope calculations suggesting 90 percent cost reduction compared to traditional methods.

    Solving the abundance paradox

    “You touched upon an important issue that I am struggling with—the abundance of guidance that my own organization produces and also guidance that comes from elsewhere,” noted a senior manager from an international humanitarian network during the briefing. “It really feels intriguing to put all that material into a course and look at what I am going to do with this. It is a precious process and really memorable and makes the policies and materials relevant.”

    This captures a central challenge facing global health organizations: not lack of knowledge, but failure to translate knowledge into action. The peer learning model transforms existing policies and guidelines into peer learning experiences where practitioners study materials to determine specific actions they will take.

    “Learning happens not simply by acquiring knowledge, but by actually doing something with it,” Sadki explained.

    For example, a collaboration with Save the Children converted a climate change policy brief into a peer learning course accessed by more than 70,000 health workers, developed and deployed in three days with initial results expected within six weeks.

    Networks that outlast the funding crisis

    The foundation’s global network now includes more than 70,000 practitioners across 137 countries, with geographic focus on nations with highest climate vulnerability and disease burden. More than 50 percent are government staff. More than 80 percent work at district and community levels.

    Tom Newton-Lewis, a leading health systems researcher and consultant who attended the briefing, captured what makes this approach distinctive: “I am always inspired by the work of TGLF. There are very few initiatives that work at scale that walk the talk on supporting local problem solving, and mobilize systems to strengthen themselves.”

    This composition ensures that peer learning initiatives operate within rather than parallel to official health systems. More than 1,000 national policy planners connect directly with field practitioners, creating feedback loops between strategy development and implementation reality.

    Networks continue functioning when external support changes. The foundation has documented continued peer connections through network analysis, confirming that established relationships maintain over time.

    Three pathways forward

    The foundation outlined entry points for organizations seeking proven implementation approaches. First, organizations can become program partners, providing their staff access to existing global programs while co-developing new initiatives. Available programs include measles, climate change and health, mental health, non-communicable diseases, neglected tropical diseases, immunization, and women’s leadership.

    Second, using the model to connect policy and implementation at scale and lower cost. Timeline: three days to build, four to six weeks for initial results. Organizations gain direct access to field innovations while receiving evidence-based feedback on what actually works in practice.

    Third, testing the model on current problems where policy exists but implementation remains inconsistent. Organizations can connect their staff to practitioners who have solved similar problems without additional funding. Timeline: six to eight weeks from start to documented results.

    The foundation operates through co-funding partnerships rather than grant-making, with flexible arrangements tailored to partner capacity and project scope. What they call “economy of effort” often delivers initiatives spanning more than 50 countries for the cost of single-country projects.

    Adaptability across contexts

    The model has demonstrated remarkable versatility across different contexts and challenges. The foundation has successfully adapted the approach to new geographic areas like Ukraine and thematic areas like mental health and psychosocial support. Each adaptation requires understanding specific contexts, needs, and goals, but the fundamental peer learning principles remain consistent.

    An Indian NGO raised a fundamental challenge: “Where we struggle with program implementation post-funding is without remuneration frontline workers. Although they want to bring change in the community, are motivated, and have enough data, cannot continue.”

    Sadki’s response: “By recognizing the capabilities for analysis, for adaptation, for carrying out more effective implementation because of what they know, because they are there every day, that should contribute to a growing movement for recognition that CHWs in particular should be paid for the work that they do.”

    The path forward

    The Nigerian health worker’s realization—discovering untapped potential in existing resources—represents more than individual transformation. It demonstrates how peer learning unlocks collective intelligence already present within communities and health systems.

    In two weeks, health workers connected with each other across Nigeria’s most challenging regions, facilitated by the foundation’s proven methodology. By the sixth week, they had begun reporting credible, measurable health improvements. The model works because it values local knowledge, creates peer support systems, and integrates with government structures rather than bypassing them.

    With funding cuts forcing difficult choices across global health, this model offers documented evidence that better health outcomes can cost less, sustainable networks continue without external support, and local solutions scale globally. For organizations seeking proven implementation approaches during resource constraints, the question is not whether they can afford to try peer learning, but whether they can afford not to.

    Image: The Geneva Learning Foundation Collection © 2025

  • When funding shrinks, impact must grow: the economic case for peer learning networks

    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 gain
    Cost per participant$1,850$26786% reduction
    Implementation rate15-20%70-80%4x higher success
    Duration of engagement2-3 days90+ days30x longer
    Post-training supportNoneContinuous networkSustained capacity

    Learn 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.

    1. 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.
    2. 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.
    3. 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

  • The business of artificial intelligence and the equity challenge

    The business of artificial intelligence and the equity challenge

    Since 2019, when The Geneva Learning Foundation (TGLF) launched its first AI pilot project, we have been exploring how the Second Machine Age is reshaping learning. Ahead of the release of the first framework for AI in global health, I had a chance to sit down with a group of Swiss business leaders at the PanoramAI conference in Lausanne on 5 June 2025 to share TGLF’s insights about the significance and potential of artificial intelligence for global health and humanitarian response. Here is the article posted by the conference to recap a few of the take-aways.

    The Global Equity Challenger

    At the Panoramai AI Summit, Reda Sadki, leader of The Geneva Learning Foundation, delivered provocative insights about AI’s impact on global equity and the future of human work. Drawing from humanitarian emergency response and global health networks, he challenged comfortable assumptions about AI’s societal implications.

    The job displacement reality

    Reda directly confronted panel optimism about job preservation: “One of the things I’ve heard from fellow panelists is this idea that we can tell employees AI is not coming for your job. And I struggle to see that as anything other than deceitful or misleading at best. ”

    Eliminating knowledge worker positions in education

    “In one of our programmes, after six months we were able to use AI to replace key functions initially performed by humans. Humans helped us figure out how to do it. We then refocused a smaller team on tasks that we cannot or do not want to automate. We tried to do this openly.”

    What’s left for humans to do?

    “These machines are already learning faster and better than us, and they are doing so exponentially. Right now, what’s left for humans currently is the facilitation, facilitating connections in a peer learning system. We do not yet have agents that can facilitate, that can read the room, that can help humans understand.”

    Global access inequities

    Reda highlighted three critical equity challenges: geographic access restrictions (‘geolocking’), transparency expectations around AI usage, and punitive accountability systems that discourage innovation in humanitarian contexts. “Somebody who uses AI in that context is more likely to be punished than rewarded, even if the outcomes are better and the costs are lower. ”

    Emerging markets disconnect

    “Even though that’s where the future markets are likely to be for AI, ” Reda observed limited engagement with Africa, Asia, and Latin America among attendees, highlighting a strategic blindness to global AI market evolution.

    Organizational evolution question

    Reda posed fundamental questions about future organizational structures, questioning whether traditional hierarchical models with management layers will remain dominant “two years or five years down the line. ”

    Network-based innovation vision

    “We’ve nurtured the emergence of a global network of health workers sharing their observations of climate change impacts on the health of communities they serve. This is already powerful for preparedness and response, but we’re trying to find ways to weave in and embed AI as co-workers and co-thinkers to help health workers harness messy, complex, large-volume climate data.”

    Exponential learning challenge

    “These machines are already learning faster and better than us and that, and they’re doing so exponentially better than us. It’s pretty clear what, you know, what keeps me awake at night is what what’s left for humans. ”

    Key Achievement: Reda demonstrated how honest assessment of AI’s transformative impact requires abandoning comfortable narratives about job preservation, positioning global leaders to address equity challenges while identifying uniquely human capabilities in an AI-augmented world.

    Reda Sadki serves as Executive Director of The Geneva Learning Foundation (TGLF), a Swiss non-profit. Concurrently, he maintains his position as Chief Learning Officer at Learning Strategies International (LSi) since 2013, where he helps international organizations improve their change execution capabilities. TGLF, under his guidance, catalyzes large-scale peer networks of frontline actors across 137 countries, developing learning experiences that transform local expertise into innovation and measurable results.

    Image: PanoramAI (Raphaël Briner).

  • Global health: learning to do more with less

    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

  • Online learning completion rates in context: Rethinking success in digital learning networks

    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:

    1. Course length: Shorter courses consistently achieved higher completion rates.
    2. Assessment type: Auto-grading showed better completion than peer assessment.
    3. 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:

    1. Participants engage primarily with peers rather than pre-recorded content.
    2. Learning is organized around actual workplace challenges rather than abstract concepts.
    3. The experience builds sustainable professional relationships rather than one-time course completion.
    4. Assessment occurs through peer review and real-world application rather than quizzes or assignments.
    5. 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:

    1. Modular design: Create shorter, more connected learning units rather than lengthy courses.
    2. Real-world integration: Link learning directly to participants’ practice contexts.
    3. Peer engagement: Provide structured opportunities for health workers to learn from each other.
    4. 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

    Sculpture: The Geneva Learning Foundation Collection © 2025

  • HPV vaccination: New learning and leadership to bridge the gap between planning and implementation

    HPV vaccination: New learning and leadership to bridge the gap between planning and implementation

    This article is based on my presentation about HPV vaccination at the 2nd National Conference on Adult Immunization and Allied Medicine of the Indian Society for Adult Immunization (ISAI), Science City, Kolkata, on 15 February 2025.

    The HPV vaccination implementation challenge

    The global landscape of HPV vaccination and cervical cancer prevention reveals a mix of progress and persistent challenges. While 144 countries have introduced HPV vaccines nationally and vaccination has shown remarkable efficacy in reducing cervical cancer incidence, significant disparities persist, particularly in low- and middle-income countries.

    Evidence suggests that challenges in implementing and sustaining HPV vaccination programs in developing countries are significantly influenced by gaps between planning at national level and execution at local levels. Multiple studies confirm this disconnect as a primary barrier to effective HPV vaccination programmes.

    Traditional approaches to knowledge development in global health often rely on expert committee models characterized by hierarchical knowledge flows, formal meeting processes, and bounded timelines. While these approaches offer strengths like high academic rigor and systematic review, they frequently miss frontline insights, develop slowly, and produce static outputs that may be difficult to translate effectively into action.

    How the peer learning network alternative can support HPV vaccination

    At The Geneva Learning Foundation (TGLF), we have developed a complementary model—one that values the collective intelligence of frontline health workers and creates structured opportunities for their insights to inform policy and practice. This peer learning network model features:

    • Large, diverse networks with multi-directional knowledge flow
    • Open participation and flexible engagement
    • Direct field experience and implementation insights
    • Iterative development through experience sharing
    • Continuous refinement and living knowledge

    This approach captures practical knowledge, enables rapid learning cycles, preserves context, and brings together multiple perspectives in a dynamic process that continuously updates as new information emerges.

    HPV vaccination: the peer learning cycle in action

    To address HPV vaccination challenges, we implemented a structured five-stage cycle that connected frontline experiences with policy decisions:

    1. Experience collection at scale: In June 2023, we engaged over 16,000 health professionals to share their HPV vaccination experiences through our Teach to Reach programme. This stage focused specifically on capturing frontline implementation challenges and solutions across diverse contexts.
    2. Synthesis and analysis: TGLF’s Insights Unit identified key themes, success patterns, and common challenges while highlighting local innovations and practical solutions that emerged from the field.
    3. Knowledge deepening: In October 2023, we conducted a second round of experience sharing that built upon earlier discussions at Teach to Reach. This stage featured more in-depth case studies and implementation stories, providing additional contexts and approaches to vaccination challenges.
    4. National-level review: In January 2024, we facilitated a consultation with national EPI (Expanded Programme on Immunization) planners from 31 countries. This created direct connections between field experience and national strategy, validating and enriching the collected insights.
    5. Knowledge mobilization: Finally, we synthesized the insights into practical guidance, ready for sharing back to frontline workers, and established a foundation for continued learning cycles.

    This process uniquely values the practical wisdom that emerges from implementation experience. Rather than assuming solutions flow from the top down, we recognize that those doing the work often develop the most effective approaches to complex challenges.

    Teach to Reach: Building a learning community for HPV vaccination

    Our Teach to Reach programme serves as the hub for this peer learning approach. Since its inception, the community has grown steadily since January 2021 to reach over 24,000 members by December 2024. The participants reflect remarkable diversity.

    This diversity of contexts and experiences creates a rich environment for learning. The programme demonstrates significant impact on participants’ professional capabilities—compared to global baselines, Teach to Reach participants show:

    • 45% stronger worldview change
    • 41% greater impact on professional practice
    • 49% higher professional influence

    7 insights about HPV vaccination from peer learning at Teach to Reach

    Through this process, we uncovered several important implementation insights:

    1. Importance of connecting field experience to policy

    • Each stage deepened understanding of implementation challenges
    • We observed progression from tactical to strategic considerations
    • Growing recognition of systemic factors emerged
    • Evolution from individual to institutional solutions became apparent
    • Value of structured knowledge sharing across levels was demonstrated

    2. Implementation learning

    • Success requires multi-stakeholder engagement
    • Sustained communication proves more effective than one-time campaigns
    • School systems provide critical implementation platforms
    • Community leadership is essential for acceptance
    • Integration with other services increases efficiency
    • Local adaptation is key to successful implementation

    3. Unexpected implementation findings

    • Tribal communities often showed less vaccine hesitancy than urban areas
    • Teachers emerged as more influential than health workers in some contexts
    • Personal stories proved more persuasive than statistical evidence
    • Integration with COVID-19 vaccination improved HPV acceptance
    • Social media played both positive and negative roles
    • School-based programs sometimes reached out-of-school children

    4. Counter-intuitive success factors

    • Less formal settings often produced better results
    • Simple communication strategies outperformed complex ones
    • Male community leaders became strong vaccination advocates
    • Religious institutions provided unexpected support
    • Health worker vaccination of own children became powerful tool
    • Community dialogue proved more effective than expert presentations

    5. Unexpected challenges

    • Urban areas sometimes showed more resistance than rural areas
    • Education level did not correlate with vaccine acceptance
    • Health workers themselves sometimes showed hesitancy
    • Traditional media was less influential than anticipated
    • Formal authority figures were not always the most effective advocates
    • Technical knowledge proved less important than communication skills

    6. Examples of novel solutions

    • Using cancer survivors as advocates
    • WhatsApp groups for community health workers
    • School children as messengers to families
    • Integration with existing women’s groups
    • Leveraging religious texts and teachings
    • Community theater and storytelling approaches

    System-level surprises

    • Success was often independent of resource levels
    • Informal networks proved more important than formal ones
    • Bottom-up strategies were more effective than top-down approaches
    • Social factors were more influential than technical ones
    • Local adaptation was more important than standardization
    • Peer influence was more powerful than expert authority

    In some cases, these findings challenge many conventional assumptions about HPV vaccination programmes. In all cases, they highlight the importance of local knowledge, social factors, and adaptation over standardized approaches based solely on technical expertise.

    The power of health worker collective intelligence

    Our approach demonstrates the value of health worker collective intelligence in improving performance:

    • High-quality data and situational intelligence from our network of 60,000+ health workers provides rapid insights
    • Field observations on changing disease patterns and resistance can be quickly collected
    • Climate change impacts can be tracked through frontline reports
    • The TGLF Insights Unit packages this intelligence into knowledge to inform practice and policy

    This represents a fundamental shift from assuming expert committees have all the answers to recognizing the distributed expertise that exists throughout health systems.

    Continuous learning: The key to improvement

    In fact, previous TGLF research has demonstrated that continuous learning is often the “Achilles’ heel” in immunization programs. Common issues include:

    1. Relative lack of learning opportunities
    2. Limited ability to experiment and take risks
    3. Low tolerance for failure
    4. Focus on task completion at the expense of building capacity for future performance
    5. Lack of encouragement for learning tied to tangible organizational incentives

    In 2020 and 2022, we conducted large-scale measurements of learning culture of more than 10,000 immunization professionals in low- and middle-income countries. The data showed that ‘learning culture’ (a measure of the capacity for change) correlated more strongly with perceived programme performance than individual motivation did. This challenges the common assumption that poor motivation is the root cause of poor performance.

    These findings help zero in on six ways to strengthen continuous learning to drive HPV vaccination:

    1. Motivate health workers to believe strongly in the importance of what they do
    2. Give them practice dealing with difficult situations they might face
    3. Build mental resilience for facing obstacles
    4. Prompt them to enlist coworkers for support
    5. Help them engage their bosses to provide guidance, support, and resources
    6. Help them identify and overcome workplace obstacles

    Impact and benefits of peer learning

    This approach delivers multiple benefits:

    • Frontline workers gain broader perspective
    • National planners access grounded insights
    • Practical solutions spread more quickly
    • Policy decisions are informed by field experience
    • Continuous improvement cycle gets established

    Key success factors include:

    • Scale that enables diverse input collection
    • Structure that supports quality knowledge creation
    • Regular rhythm that maintains engagement
    • Multiple levels of review that ensure relevance
    • Clear pathways from insight to action

    How can we interpret these findings?

    This model generates implementation-focused evidence that complements rather than competes with traditional epidemiological data. 

    The findings emerge from a structured methodology that includes initial experience collection at scale, synthesis and analysis, knowledge deepening through case studies, national-level review by EPI planners from 31 countries, and systematic knowledge mobilization. This approach provides rigor and scale that elevate these observations beyond mere anecdotes.

    For epidemiologists who become uncomfortable when evidence is not purely quantitative, it is important to understand that structured peer learning fills a critical gap in implementation science by capturing what quantitative studies often miss: the contextual factors and practical adaptations that determine programme success or failure in real-world settings.

    When implementers report across different contexts that tribal communities show less vaccine hesitancy than urban areas, or that teachers emerge as more influential than health workers in specific settings, these patterns represent valuable implementation intelligence.

    Such insights also help explain why interventions that appear effective in controlled studies often fail to deliver similar results when implemented at scale.

    In fact, these findings address precisely what quantitative studies struggle to capture: why education level does not reliably predict vaccine acceptance; why some resource-constrained settings outperform better-resourced ones; how informal networks frequently prove more effective than formal structures; and which communication approaches actually drive behavior change in specific populations.

    For programme planners, this knowledge bridges the gap between general guidance (“engage community leaders”) and actionable specifics (“male community leaders became particularly effective advocates when engaged through these specific approaches”). 

    Accelerating HPV vaccination progress

    To make significant progress on HPV vaccination as part of the Immunization Agenda 2030’s Strategic Priority 4 (life-course and integration), we encourage global health stakeholders to:

    1. Rethink how we learn
    2. Question how we engage with families and communities
    3. Focus on trust

    By combining expert knowledge with the practical wisdom of thousands of implementers, we can develop more effective strategies for HPV vaccination that bridge the gap between planning and execution.

    This peer learning network approach does not replace expertise—it enhances and grounds it in the realities of implementation.

    It recognizes that the frontline health worker in a remote village may hold insights just as valuable as those of a technical expert in a capital city.

    By creating structures that enable these insights to emerge and connect, we can accelerate progress on HPV vaccination and other public health challenges.

    Acknowledgements

    I wish to thank ISAI’s Dr Saurabh Kole and his colleagues for their kind invitation. I also wish to recognize and appreciate Charlotte Mbuh and Ian Jones for their invaluable contributions to the Foundation’s work on HPV vaccination, and Dr Satabdi Mitra for her tireless leadership and boundless commitment. Last but not least, I wish to thank the thousands of health workers who contributed their experiences before, during, and after successive Teach to Reach peer learning events. What little I know comes from their collective intelligence, action, and wisdom.

    References

    Dorji, T. et al. (2021) ‘Human papillomavirus vaccination uptake in low-and middle-income countries: a meta-analysis’, EClinicalMedicine, 34, p. 100836. Available at: https://doi.org/10.1016/j.eclinm.2021.100836.

    Faye, W. et al. (2023) IA2030 Case study 18. Wasnam Faye. Vaccine angels – Give us the opportunity and we can perform miracles. The Geneva Learning Foundation. Immunization Agenda 2030 Case study 18. Available at: https://doi.org/10.5281/ZENODO.7785244.

    Gonçalves, I.M.B. et al. (2020) ‘HPV Vaccination in Young Girls from Developing Countries: What Are the Barriers for Its Implementation? A Systematic Review’, Health, 12(06), pp. 671–693. Available at: https://doi.org/10.4236/health.2020.126050.

    Jones, I. et al. (2024) Making connections at Teach to Reach 8 (IA2030 Listening and Learning Report 6). Available at: https://doi.org/10.5281/ZENODO.8398550.

    Jones, I. et al. (2022) IA2030 Case Study 7. Motivation, learning culture and programme performance. The Geneva Learning Foundation. Available at: https://doi.org/10.5281/ZENODO.7004304.

    Kutz, J.-M. et al. (2023) ‘Barriers and facilitators of HPV vaccination in sub-saharan Africa: a systematic review’, BMC Public Health, 23(1), p. 974. Available at: https://doi.org/10.1186/s12889-023-15842-1.

    Moore, K. et al. (2022) Overcoming barriers to vaccine acceptance in the community: Key learning from the experiences of 734 frontline health workers. The Geneva Learning Foundation. Available at: https://doi.org/10.5281/ZENODO.6965355.

    Umbelino-Walker, I. et al. (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. Edited by M. Pentecost, 4(12), p. e0003855. Available at: https://doi.org/10.1371/journal.pgph.0003855.

    Watkins, K.E. et al. (2022) ‘Accelerating problem-solving capacities of sub-national public health professionals: an evaluation of a digital immunization training intervention’, BMC Health Services Research, 22(1), p. 736. Available at: https://doi.org/10.1186/s12913-022-08138-4.

    Wigle, J., Coast, E. and Watson-Jones, D. (2013) ‘Human papillomavirus (HPV) vaccine implementation in low and middle-income countries (LMICs): Health system experiences and prospects’, Vaccine, 31(37), pp. 3811–3817. Available at: https://doi.org/10.1016/j.vaccine.2013.06.016.

  • A global health framework for Artificial Intelligence as co-worker to support networked learning and local action

    A global health framework for Artificial Intelligence as co-worker to support networked learning and local action

    The theme of International Education Day 2025, “AI and education: Preserving human agency in a world of automation,” invites critical examination of how artificial intelligence might enhance rather than replace human capabilities in learning and leadership. Global health education offers a compelling context for exploring this question, as mounting challenges from climate change to persistent inequities demand new approaches to building collective capability.

    The promise of connected communities

    Recent experiences like the Teach to Reach initiative demonstrate the potential of structured peer learning networks. The platform has connected over 60,000 health workers, primarily government workers from districts and facilities across 82 countries, including those serving in conflict zones, remote rural areas, and urban settlements. For example, their exchanges about climate change impacts on community health point the way toward more distributed forms of knowledge creation in global health. 

    Analysis of these networks suggests possibilities for integrating artificial intelligence not merely as tools but as active partners in learning and action. However, realizing this potential requires careful attention to how AI capabilities might enhance rather than disrupt the human connections that drive current success.

    Artificial Intelligence (AI) partnership could provide crucial support for tackling mounting challenges. More importantly, they could help pioneer new approaches to learning and action that genuinely serve community needs while advancing our understanding of how human and machine intelligence might work together in service of global health.

    Understanding Artificial Intelligence (AI) as partner, not tool

    The distinction between AI tools and AI partners merits careful examination. Early AI applications in global health primarily automate existing processes – analyzing data, delivering content, or providing recommendations. While valuable, this tool-based approach maintains clear separation between human and machine capabilities.

    AI partnership suggests a different relationship, where artificial intelligence participates actively in learning networks alongside human practitioners. This could mean AI systems that:

    • Engage in dialogue with health workers about local observations
    • Help validate emerging insights through pattern analysis
    • Support adaptation of solutions across contexts
    • Facilitate connections between practitioners facing similar challenges

    The key difference lies in moving from algorithmic recommendations to collaborative intelligence that combines human wisdom with machine capabilities.

    A framework for AI partnership in global health

    Analysis of current peer learning networks suggests several dimensions where AI partnership could enhance collective capabilities:

    • Knowledge creation: Current peer learning networks enable health workers to share observations and experiences across borders. AI partners could enrich this process by engaging in dialogue about patterns and connections, while preserving the central role of human judgment in validating insights.
    • Learning process: Teach to Reach demonstrates how structured peer learning accelerates knowledge sharing and adaptation. AI could participate in these networks by contributing additional perspectives, supporting rapid synthesis of experiences, and helping identify promising practices.
    • Local leadership: Health workers develop and implement solutions based on deep understanding of community needs. AI partnership could enhance decision-making by exploring options, modeling potential outcomes, and validating approaches while maintaining human agency.
    • Network formation: Digital platforms currently enable lateral connections between health workers across regions. AI could actively facilitate network development by identifying valuable connections and supporting knowledge flow across boundaries.
    • Implementation support: Peer review and structured feedback drive current learning-to-action cycles. AI partners could engage in ongoing dialogue about implementation challenges while preserving the essential role of human judgment in local contexts.
    • Evidence generation: Networks document experiences and outcomes through structured processes. AI collaboration could help develop and test hypotheses about effective practices while maintaining focus on locally-relevant evidence.

    Applications across three global health challenges

    This framework suggests new possibilities for addressing persistent challenges.

    1. Immunization systems

    Current global immunization goals face significant obstacles in reaching zero-dose children and strengthening routine services. AI partnership could enhance efforts by:

    • Supporting microplanning by mediating dialogue about local barriers
    • Facilitating rapid learning about successful engagement strategies
    • Enabling coordinated action across health system levels
    • Modeling potential impacts of different intervention approaches

    2. Neglected Tropical Diseases (NTDs)

    The fight against NTDs suffers from critical information gaps and weak coordination at local levels. Many communities, including health workers, lack basic knowledge about these diseases. AI partnership could help address these gaps through:

    • Facilitating knowledge flow between affected communities
    • Supporting coordination of control efforts
    • Enabling rapid validation of successful approaches
    • Strengthening surveillance and response networks

    3. Climate change and health

    Health workers’ observations of climate impacts on community health provide crucial early warning of emerging threats. AI partnership could enhance response capability by:

    • Engaging in dialogue about changing disease patterns
    • Supporting rapid sharing of adaptation strategies
    • Facilitating coordinated action across regions
    • Modeling potential impacts of interventions

    Pandemic preparedness beyond early warning

    The experience of digital health networks during recent disease outbreaks reveals both the power of distributed response capabilities and the potential for enhancement through AI partnership. When COVID-19 emerged, networks of health workers demonstrated remarkable ability to rapidly share insights and adapt practices. For example, the Geneva Learning Foundation’s COVID-19 Peer Hub connected over 6,000 frontline health professionals who collectively generated and implemented recovery strategies at rates seven times faster than isolated efforts.

    This networked response capability suggests new possibilities for pandemic preparedness that combines human and machine intelligence. Heightened preparedness could emerge from the interaction between health workers, communities, and AI partners engaged in continuous learning and adaptation.

    Current pandemic preparedness emphasizes early detection through formal surveillance. However, health workers in local communities often observe concerning patterns before these register in official systems.

    AI partnership could enhance this distributed sensing capability while maintaining its grounding in local realities. Rather than simply analyzing reports, AI systems could engage in ongoing dialogue with health workers about their observations, helping to:

    • Explore possible patterns and connections
    • Test hypotheses about emerging threats
    • Model potential trajectories
    • Identify similar experiences across regions

    The key lies in combining human judgment about local significance with AI capabilities for pattern recognition across larger scales.

    The focus remains on accelerating locally-led learning rather than imposing standardized solutions.

    Perhaps most importantly, AI partnership could enhance the collective intelligence that emerges when practitioners work together to implement solutions. Current networks enable health workers to share implementation experiences and adapt strategies to local contexts. Adding AI capabilities could support this through:

    • Ongoing dialogue about implementation challenges
    • Analysis of patterns in successful adaptation
    • Support for rapid testing of modifications
    • Facilitation of cross-context learning

    Success requires maintaining human agency in implementation while leveraging machine capabilities to strengthen collective problem-solving.

    This networked vision of pandemic preparedness, enhanced through AI partnership, represents a fundamental shift from current approaches. Rather than attempting to predict and control outbreaks through centralized systems, it suggests building distributed capabilities for continuous learning and adaptation. The experience of existing health worker networks provides a foundation for this transformation, while artificial intelligence offers new possibilities for strengthening collective response capabilities.

    Investment for innovation

    Realizing this vision requires strategic investment in:

    • Network development: Supporting growth of peer learning platforms that accelerate local action while maintaining focus on human connection.
    • AI partnership innovation: Developing systems designed to participate in learning networks while preserving human agency.
    • Implementation research: Studying how AI partnership affects collective capabilities and health outcomes.
    • Capacity strengthening: Building health worker capabilities to effectively collaborate with AI while maintaining critical judgment.

    Looking forward

    The transformation of global health learning requires moving beyond both conventional practices of technical assistance and simple automation. Experience with peer learning networks demonstrates what becomes possible when health workers connect to share knowledge and drive change.

    Adding artificial intelligence as partners in these networks – rather than replacements for human connection – could enhance collective capabilities to protect community health. However, success requires careful attention to maintaining human agency while leveraging technology to strengthen rather than supplant local leadership.

    7 key principles for AI partnership

    1. Maintain human agency in decision-making
    2. Support rather than replace local leadership
    3. Enhance collective intelligence
    4. Enable rapid learning and adaptation
    5. Preserve context sensitivity
    6. Facilitate knowledge flow across boundaries
    7. Build sustainable learning systems

    Listen to an AI-generated podcast about this article

    🤖 This podcast was generated by AI, discussing Reda Sadki’s 24 January 2025 article “A global health framework for Artificial Intelligence as co-worker to support networked learning and local action”. While the conversation is AI-generated, the framework and examples discussed are based on the published article.

    Framework: AI partnership for learning and local action in global health

    DimensionCurrent StateAI as ToolsAI as PartnersPotential Impact
    Knowledge creationHealth workers share observations and experiences through peer networksAI analyzes patterns in shared dataAI engages in dialogue with health workers, asking questions, suggesting connections, validating insightsNew forms of collective intelligence combining human and machine capabilities
    Learning processStructured peer learning through digital platforms and networksAI delivers content and analyzes performanceAI participates in peer learning networks, contributes insights, supports adaptationAccelerated learning through human-AI collaboration
    Local leadershipHealth workers develop and implement solutions for community challengesAI provides recommendations based on data analysisAI works alongside local leaders to explore options, model scenarios, validate approachesEnhanced decision-making combining local wisdom with AI capabilities
    Network formationLateral connections between health workers across regionsAI matches similar profiles or challengesAI actively facilitates network development, identifies valuable connectionsMore effective knowledge networks leveraging both human and machine intelligence
    Implementation supportPeer review and structured feedback on action plansAI checks plans against best practicesAI engages in iterative dialogue about implementation challenges and solutionsImproved implementation through combined human-AI problem-solving
    Evidence generationDocumentation of experiences and outcomes through structured processesAI analyzes implementation dataAI collaborates with health workers to develop and test hypotheses about what worksNew approaches to generating practice-based evidence

    Image: The Geneva Learning Foundation Collection © 2024