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  • 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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  • What does AI reasoning mean for global health?

    What does AI reasoning mean for global health?

    When epidemiologists investigate a disease outbreak, they do not just match symptoms to known pathogens. They work through complex chains of evidence, test hypotheses, reconsider assumptions when data does not fit, and sometimes completely change their approach based on new information. This deeply human process of systematic reasoning is what artificial intelligence systems are now learning to do.

    This capability represents a fundamental shift from AI that recognizes patterns to AI that can work through complex problems the way a skilled professional would. For those working in global health and education, understanding this transformation is essential.

    The difference between answering and reasoning

    To understand this revolution, consider how most AI works today versus how reasoning AI operates.

    Traditional AI excels at pattern recognition. Show it a chest X-ray, and it can identify pneumonia by matching patterns it learned from millions of examples. Ask it about disease symptoms, and it retrieves information from its training data. This is sophisticated, but it is fundamentally different from reasoning.

    Consider this scenario: An unusual cluster of respiratory illness appears in a rural community. The symptoms partially match several known diseases but perfectly match none. Environmental factors are unclear. Some patients respond to standard treatments. Others do not.

    A pattern-matching AI might list possible diseases based on symptom similarity. But a reasoning AI would approach it like an epidemiologist:

    • “Let me examine the symptom progression timeline.”
    • “The geographic clustering suggests environmental or infectious cause. Let me investigate both paths.”
    • “Wait, these treatment responses do not align with any single pathogen. Could this be co-infection?”
    • “I need to reconsider. What if the environmental factor is not the cause but is affecting treatment efficacy?”

    The AI actually works through the problem, forms hypotheses, recognizes when evidence contradicts its assumptions, and adjusts its approach accordingly.

    How reasoning AI thinks through problems

    Advanced AI systems now demonstrate visible thinking processes. When analyzing complex health data, they might:

    • “First, let me identify the key variables affecting disease transmission in this population.”
    • “I will start by calculating the basic reproduction number using standard methods.”
    • “These results seem inconsistent with the observed spread pattern. Let me check my assumptions.”
    • “I may have overlooked the role of asymptomatic carriers. Let me recalculate.”
    • “This aligns better with observations. Now I can project intervention outcomes.”

    This is not scripted behavior. The AI works through problems, recognizes errors, and corrects its approach—much like a researcher reviewing their analysis.

    Why reasoning requires massive computational power

    Reasoning AI systems require thousands of times more computational resources than traditional AI. Understanding why helps explain both their power and limitations.

    Think about the difference between recognizing a disease from symptoms versus investigating a novel outbreak. Recognition happens quickly: an experienced clinician identifies malaria almost instantly. But investigating an unusual disease cluster requires sustained analysis, exploring multiple hypotheses, checking each against evidence.

    The same applies to AI. Traditional pattern-matching AI makes a single pass through its neural network. But reasoning AI must:

    • Explore multiple hypotheses simultaneously;
    • Check each reasoning step for logical consistency;
    • Backtrack when evidence contradicts assumptions;
    • Verify conclusions against all available data; and
    • Consider alternative explanations.

    Each step requires intensive computation. The AI might explore hundreds of reasoning paths before reaching sound conclusions.

    Matching expert performance

    AI systems in mid-2025 perform at the level of graduate students in mathematics and other fields. For global health, this means AI that can:

    • Design epidemiological studies with appropriate controls;
    • Identify confounding variables in complex datasets;
    • Recognize when standard statistical methods do not apply; and
    • Develop novel approaches to emerging health challenges.

    This is not about calculating faster—computers have done that for decades. It is about understanding concepts, recognizing which analytical techniques to apply, and working through novel problems.

    Applications in global health

    Reasoning AI transforms multiple aspects of global health work:

    Outbreak investigation: AI that can integrate diverse data sources—clinical reports, environmental data, travel patterns, genetic sequences—to identify outbreak sources and transmission patterns.

    Treatment optimization: Systems that reason through drug interactions, comorbidities, and local factors to recommend personalized treatment protocols.

    Resource allocation: AI that understands trade-offs between prevention and treatment, immediate needs and long-term capacity building, to optimize limited resources.

    Research design: Systems that can identify weaknesses in study designs, suggest improvements, and recognize when findings may not generalize to other populations.

    Policy analysis: AI that reasons through complex interventions, anticipating unintended consequences and identifying implementation barriers.

    What makes AI reasoning different

    Five capabilities distinguish reasoning AI from pattern-matching systems:

    1. Working memory: Reasoning AI holds multiple pieces of information active while working through problems, like a human tracking several hypotheses simultaneously.
    2. Logical consistency: Each conclusion must follow logically from evidence and prior reasoning steps.
    3. Error recognition: When results do not make sense, the system recognizes the problem and adjusts its approach.
    4. Abstraction: The AI recognizes general principles and applies them to specific situations, not just memorizing solutions.
    5. Explanation: Reasoning AI can explain its logic, making its conclusions verifiable and trustworthy.

    The path forward

    The reasoning revolution does not replace human expertise but augments it in powerful ways. For global health professionals, this means:

    • AI partners that can work through complex epidemiological puzzles;
    • Systems that help design culturally appropriate interventions;
    • Tools that identify patterns humans might miss while respecting local knowledge.

    Understanding reasoning AI is no longer optional for those shaping global health. These systems are becoming intellectual partners capable of working through complex problems alongside human experts. The question is not whether to engage with this technology but how to use it effectively while maintaining human agency, judgment, and values in decisions that affect human lives.

    The ability to reason—to work systematically through complex problems—has always been central to advancing human health and knowledge. Now that machines are learning this capability, we must thoughtfully consider how to harness it for global benefit while ensuring human wisdom guides its application.

  • 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

  • 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

  • Patterns of prejudice: Connecting the dots helps health workers combat bias worldwide

    Patterns of prejudice: Connecting the dots helps health workers combat bias worldwide

    English | Français

    “I noticed that every time he went to appointments or emergency services, he was often met with suspicion or treated as if he was exaggerating his symptoms,” shared a community support worker from Canada, describing how an Indigenous teenager waited three months for mental health services while non-Indigenous youth were seen within weeks.

    This testimony was just one of hundreds shared during an unusual global gathering where frontline health workers confronted an uncomfortable truth: healthcare systems worldwide are riddled with biases that determine who lives and who dies.

    “Equity Matters: A Practical Approach to Identify and Eliminate Biases,” a special event hosted by the Geneva Learning Foundation (TGLF) on 10-11 April 2025, drew nearly 5,000 health professionals from 72 countries. What made the event distinctive wasn’t just its scope, but its approach: creating a forum where community health workers from rural Nigeria could share insights alongside WHO officials from Switzerland, where district nurses from South Sudan could analyze cases with medical college professors from India.

    When healthcare isn’t equal: Global patterns emerge

    Despite working in vastly different contexts, participants described remarkably similar patterns of bias.

    “A pregnant woman was about to deliver in the hospital, but the doctor said they need to deposit 500,000 naira before she can touch the woman,” recounted Onosi Chikaodiri Peter, a community health worker with Light Bringer’s Outreach in Nigeria. “The husband was begging, pleading, with 100,000 naira, telling the doctor that he could sell all his livestock to make sure that the wife was okay. But the doctor wouldn’t attend to the woman. Along the line, the woman gave up. The child died.”

    Dr. Tusiime Ramadhan, who works with Humanitarian Volunteers International in Uganda, observed the same pattern: “People with money are referred to private clinics and hospitals for better health services often owned by the same government workers who sent them there.”

    Some biases manifest in subtler ways. Hussainah Abba Ali, who works with Impact Santé Afrique in Cameroon, described seeking treatment for malaria during her university years: “Because I was a young woman, the nurse assumed I was just exaggerating. She barely examined me, gave me paracetamol and told me to rest. I later found out that several men who came in after me with similar symptoms were tested immediately for malaria.”

    The stories came from everywhere—a physiotherapist in Nigeria whose expertise was ignored in favor of a male colleague; a nutritionist in DR Congo whose albino neighbor avoided vaccination clinics because of stigma; a public health specialist in Ethiopia’s Somali Region who explained how healthcare systems are designed for settled communities, leaving pastoralist populations behind.

    Alina Onica, a psychologist with Romania’s Icar Foundation working with domestic violence survivors, noted: “Victims are often judged for ‘not leaving’ the abuser, as if staying means it’s not serious. This bias ignores the complex trauma and fear they live with every day.”

    A framework for sense-making beyond single-issue analysis

    What united these diverse testimonies was the application of the BIAS FREE Framework, a practical tool that helps identify and eliminate discriminatory patterns in health systems.

    “Margaret Eichler and I started this work back in 1995 after developing some gender-based analysis tools,” explained Mary Anne Burke, the framework’s co-author. “We realized we had created something that could be applied to all social hierarchies. We’ve workshopped it on every continent but Antarctica and found it applicable everywhere.”

    Unlike approaches that focus exclusively on gender, ethnicity, or disability, the BIAS FREE Framework examines how these factors intersect. Brigid Burke, a researcher who’s used and taught the framework for 15 years, explained how to identify three distinct problem types:

    • H problems: Where existing hierarchies are maintained
    • F problems: Where relevant differences between groups are ignored
    • D problems: Where different standards are applied to different groups

    “It is easier to understand a hierarchy when you’re experiencing the oppression,” Burke told participants. “You can feel that you’re being treated in a way that takes away your dignity. It’s harder when you might be the one who is either consciously or unconsciously oppressing other people.”

    During the event, participants first shared their own experiences, then began to analyze them using the framework. Abdoulie Bah, a regional Red Cross officer from The Gambia, offered his analysis: “Oppressive hierarchies suggest that certain groups experience more oppression than others, often leading to a competitive dynamic among marginalized groups.”

    Solutions from the ground up

    What distinguished this event from typical global health conferences was its emphasis on solutions developed by frontline workers themselves.

    Dr. Orimbato Raharijaona, a medical doctor from Madagascar, described his team’s efforts to reach children in remote areas: “We prioritized areas with low vaccination coverage and strengthened birth follow-up to target zero-doses. Community dialogue helped raise awareness of the need for vaccination.”

    In Mali, Bouréma Mounkoro, a public health medical assistant, discovered that simply rescheduling vaccination days to align with community availability dramatically improved coverage rates and reduced dropouts.

    Dayambo Yendoukoua from Niger’s Red Cross developed an integrated approach addressing rural women’s exclusion from maternal care: “Women from villages and farming hamlets have three times less access to obstetric care than urban women. We grouped women into Mothers’ Clubs, provided literacy training, set up income-generating activities, and established traditional ambulances managed by women.”

    This emphasis on community-based solutions resonated with Esther Y. Yakubu, a health worker with the Health and Development Support Programme in Nigeria: “This program will surely be of great value in the health sector. If put in place, it will make a huge difference and patients will receive quality treatment without any segregations.”

    Practical action – not academic debates – to decolonize global health

    The event itself embodied the principles it aimed to teach. Rather than positioning Western experts as authorities, TGLF structured the event to value diverse forms of expertise.

    “Community health workers can see barriers that researchers miss. Global researchers spot patterns invisible at the local level. Policy makers understand system constraints that affect implementation,” explained Reda Sadki, TGLF’s Executive Director. “It’s when these perspectives connect that we find better solutions.”

    On 24-25 April 2025, this community will reconvene to determine if there is enough interest and momentum to launch the Foundation’s Certificate peer learning programme for equity in research and practice. An inaugural course could be launched as early as June 2025.

    “Your participation helps determine if we develop a full program on identifying and removing bias in health systems,” TGLF explained in its materials. “When more than 1,000 people participate, it shows enough interest to create a more comprehensive learning opportunity.”

    The certificate program will bring together participants from across professional hierarchies—community health workers, district managers, national planners, and global researchers—creating a rare space where knowledge flows in all directions.

    Across time zones and contexts, the conversation highlighted a shared understanding: addressing bias in healthcare isn’t just about fairness—it’s about survival. As Haske Akiti Joseph, a radiographer from Nigeria’s National Orthopaedic Hospital, reflected: “These issues are happening everywhere because governments will not provide free medical services to the people, and medical considerations come due to who you are, not based on priority.”

    In a world where your chances of receiving timely, appropriate healthcare often depend on your gender, ethnicity, wealth, or location, the BIAS FREE Framework offers a practical way forward—one that begins with recognizing patterns of oppression that transcend borders and cultures.

    Image: The Geneva Learning Foundation Collection © 2025

  • L’équité compte: quand les soignants du monde entier témoignent des inégalités en santé

    L’équité compte: quand les soignants du monde entier témoignent des inégalités en santé

    English | Français

    GENÈVE, le 11 avril 2025 – Une initiative internationale inédite a rassemblé près de 5000 professionnels de santé pour partager leurs expériences face aux discriminations dans l’accès aux soins

    « Un enfant est mort parce que sa famille ne pouvait pas déposer 500 000 nairas [environ 300 francs suisses] avant le début des soins. Le père avait pourtant supplié qu’on s’occupe de l’enfant, proposant 100 000 nairas et promettant de vendre son bétail pour payer le reste. » Ce récit glaçant d’un professionnel de santé nigérian illustre la dure réalité des inégalités d’accès aux soins dont de nombreux témoignages ont été partagés lors d’un événement international consacré à l’équité en santé.

    Le 11 avril dernier, la Fondation Apprendre Genève a créé un espace de dialogue sans précédent, rassemblant près de 5 000 professionnels de la santé de 72 pays, dont 1 830 francophones. Intitulé « L’équité compte: une approche pratique pour identifier et éliminer les biais », cet événement a permis à des médecins, infirmiers, agents de santé communautaires et autres acteurs du terrain de raconter, dans leurs propres mots, les discriminations qu’ils observent quotidiennement.

    Des récits convergents malgré la diversité des contextes

    « L’originalité de cette rencontre réside dans sa capacité à faire émerger des expériences habituellement invisibilisées », explique Reda Sadki, directeur exécutif de la Fondation. « Des praticiens qui n’ont jamais accès aux tribunes internationales ont pu témoigner des réalités qu’ils affrontent chaque jour. »

    Ces témoignages, remarquablement similaires malgré la diversité des contextes, révèlent que le statut social détermine encore largement la qualité et la rapidité des soins. « Nous avions amené un enfant gravement malade à l’hôpital », raconte Neville Kasongo, du Corps des jeunes contre le paludisme en République démocratique du Congo. « Pendant que nous attendions plus de six heures, j’ai vu notre voisin arriver avec son enfant malade. Comme il avait des relations particulières dans cette institution, les cadres soignants se sont précipités pour s’occuper de son fils. Pour nous qui n’avions aucune connexion, quand ils sont finalement venus, l’enfant était déjà très affaibli. Une heure après, il est décédé. »

    Brigitte Meugang, point focal du Programme élargi de vaccination au Cameroun, a observé un phénomène similaire lors d’une visite à l’hôpital: « J’avais un malade hospitalisé et je suis arrivée un peu en retard pendant les heures de visite. Le vigile m’a dit: “Tu n’entres pas parce que l’heure de visite est déjà passée.” Quelques minutes plus tard, un cousin militaire est arrivé en tenue. Le vigile a ouvert le portail et lui a dit d’entrer. » Quand elle a demandé pourquoi, on lui a répondu qu’il était en uniforme. C’est seulement après avoir présenté sa carte professionnelle qu’elle a été autorisée à entrer.

    Les intervenants ont également souligné comment des groupes entiers sont systématiquement laissés pour compte. « Dans les zones de conflit au Burkina Faso, les femmes, les enfants et les personnes âgées déplacés subissent des violences basées sur le genre car leurs besoins spécifiques ne sont pas pris en compte », témoigne une spécialiste genre et inclusion sociale. « Les enfants souffrent de malnutrition, les femmes enceintes n’ont pas accès aux consultations prénatales, et les personnes âgées ne bénéficient pas de soins adaptés. »

    Quand l’injustice touche même les soignants

    Particulièrement frappants sont les témoignages de professionnels de santé ayant eux-mêmes subi des discriminations. Le Dr Balkissa Modibo Hama, coordonnatrice du programme mondial d’éradication de la poliomyélite pour l’OMS en Guinée, raconte: « Lors de l’accouchement de ma seconde fille, le personnel ne s’est pas occupé de moi jusqu’à ce que la sage-femme responsable arrive et leur dise qui j’étais. Soudain, tous se sont mobilisés autour de moi en me reprochant de ne pas m’être présentée. Après mon accouchement, j’ai convoqué tout le personnel pour les sensibiliser sur le fait qu’on ne devrait pas avoir besoin de dire qui on est pour recevoir des soins de qualité. »

    Dans certains cas, c’est l’expérience personnelle de l’injustice qui a motivé l’engagement professionnel. « À 13 ans, j’ai accompagné ma mère à l’hôpital », poursuit le Dr Hama. « L’infirmière, qui connaissait ma mère, a voulu me faire passer avant une femme Bororo dont l’enfant était plus mal en point. J’ai refusé, mais j’ai ensuite constaté que cette femme et son enfant avaient été négligés. Cette expérience m’a profondément marquée et a motivé ma décision de devenir médecin. »

    Christian Kpoyablé Clahin, infirmier en Côte d’Ivoire, a partagé un cas tragique: « Une femme est venue avec son enfant gravement malade. Elle n’avait pas d’argent pour payer les analyses. L’enfant a été mis à l’écart au laboratoire et cela a traîné jusqu’à ce qu’il soit trop tard. L’enfant est mort. J’ai interpellé le directeur de l’hôpital, mais les sanctions n’ont été que verbales. »

    Des initiatives locales qui font la différence

    Au-delà du constat, les participants ont partagé des solutions concrètes qu’ils ont développées face à ces inégalités. Arthur Fidelis Metsampito Bamlatol, coordinateur d’une association de santé au Cameroun, explique: « J’avais observé que les enfants Baka [pygmées] étaient insuffisamment vaccinés. Après avoir signalé ce problème au médecin-chef de district, nous avons cartographié les campements dans la forêt et institué des stratégies spéciales. Lors des campagnes suivantes, nous marchions parfois plusieurs heures à pied pour atteindre ces communautés isolées. »

    D’autres adaptations créatives ont été mentionnées, comme celle rapportée par Bouréma Mounkoro, assistant médical au Mali: « Le planning des activités de vaccination n’était pas synchronisé avec la disponibilité de la communauté. Nous avons reprogrammé les jours de vaccination en tenant compte des réalités locales, ce qui a amélioré la couverture vaccinale et réduit considérablement les cas d’abandon. »

    Pour Brice Alain Dakam Ncheuta, responsable de l’engagement communautaire à Médecins Sans Frontières au Niger, comprendre les dynamiques culturelles est essentiel: « Dans le Grand Sahel, pour réduire les biais dans la prise en charge des violences basées sur le genre, nous travaillons étroitement avec les leaders communautaires. Nous proposons des soins médicaux sans heurter la sensibilité culturelle, car cela fait partie de l’identité des personnes que nous accompagnons. »

    Les solutions peuvent parfois être simples mais révolutionnaires, comme l’illustre l’initiative de Dayambo Yendoukoua, délégué de programme santé à la Croix-Rouge au Niger: « Dans les villages et hameaux agricoles, nous avons constaté que les femmes ont trois fois moins accès aux soins obstétricaux que les femmes urbaines. Nous avons créé des Clubs de Mères, offert des formations d’alphabétisation, mis en place des activités génératrices de revenus, et établi des ambulances traditionnelles gérées par les femmes elles-mêmes. »

    Vers un partage de savoirs plus équitable

    L’originalité de cet événement réside également dans sa méthodologie même. Plutôt que de suivre le schéma classique des conférences internationales où les experts occidentaux partagent leur savoir avec les praticiens du Sud, la Fondation Apprendre Genève a délibérément inversé cette logique. « Ce sont les professionnels de terrain qui ont pris la parole en premier », souligne Reda Sadki, directeur exécutif de la Fondation.

    « Les agents de santé communautaire peuvent voir des obstacles que les chercheurs manquent. Les décideurs comprennent les contraintes systémiques qui affectent la mise en œuvre des politiques. C’est lorsque ces perspectives se connectent que nous trouvons de meilleures solutions », poursuit-il.

    Pour faciliter l’analyse de ces expériences, Brigid Burke a accompagné la rencontre en tant que Guide. Burke est une chercheuse spécialisée dans le cadre BIAS FREE, un outil développé par Mary-Anne Burke et Margaret Eichler, permettant d’identifier différents types de biais. Cela a permis d’aller au-delà des constats en proposant une grille d’analyse des échanges entre participants qui ont constitué le cœur de la rencontre.

    Le succès de cette approche pourrait conduire à la création d’un programme de formation international, dont le lancement sera discuté lors d’une nouvelle rencontre fin avril. « Nous souhaitons développer un espace où les connaissances circulent véritablement dans toutes les directions, plutôt que du Nord vers le Sud », précise M. Sadki.

    La participation massive à cet événement – bien au-delà des attentes des organisateurs – témoigne d’un besoin urgent d’aborder ces questions. « Votre participation aide à déterminer si nous développons un programme plus complet sur ces questions », a expliqué la Fondation. « Quand près de 5000 personnes participent, cela montre qu’il y a suffisamment d’intérêt. »

    « La meilleure stratégie pour corriger tous les biais reste l’installation partout dans nos pays d’une couverture maladie universelle », suggère le Dr Oumar Traoré, médecin de santé publique en Guinée. Une vision à laquelle fait écho Amadou Gueye, président du Malaria Youth Corps en Guinée: « Ces témoignages nous rappellent que l’équité en santé n’est pas qu’une question technique, mais aussi une question de justice fondamentale. »

    Image: Collection de la Fondation Apprendre Genève © 2025

  • Chilling effect

    Chilling effect

    We reached out to senior decision makers working in global health about the new Certificate peer learning programme for equity in research and practice.

    Crickets.

    One CEO wrote: “We aren’t currently in a position to enter into new strategic partnerships on the topic.”

    The chilling effect is real.

    Many organizations are retreating from publicly championing equity work—even those with deep commitments to fairness and inclusion.

    But here’s the opportunity: While public discourse faces headwinds, meaningful work continues through trusted networks and communities of practice.

    This is precisely when innovation in equity approaches accelerates—away from the spotlight but with profound impact.

    The evidence is clear: health systems that neglect equity waste resources and deliver poorer outcomes.

    When research excludes key populations or policies overlook certain communities, we all lose—through inefficiency, increased costs, and diminished impact.

    This moment calls for courage from those who understand that equity is fundamental to effective health systems.

    “The ultimate measure of a person is not where they stand in moments of comfort, but where they stand at times of challenge.” – Martin Luther King Jr.

    If you’re still committed to this essential work, you’re not alone.

    Question: How are you maintaining momentum on equity work during challenging times?

    Image: The Geneva Learning Foundation Collection © 2025

  • What is complex learning?

    What is complex learning?

    Complex learning happens when people solve real problems instead of just memorizing facts.

    Think about the difference between reading about how to ride a bicycle and actually learning to ride one.

    You cannot learn to ride a bicycle just by reading about it – you need to practice, fall, adjust, and try again until your body understands how to balance.

    Health challenges work the same way.

    Reading about how to respond to a disease outbreak is very different from actually managing one.

    Complex learning recognizes this difference.

    5 key features of complex learning:

    1. Learning by doing: People learn best when they work on real problems they face in their jobs. Instead of just listening to experts, they actively try solutions, see what works, and adjust their approach.
    2. No single right answer: Complex learning deals with situations where there is no perfect solution that works everywhere. What works in one community might fail in another because of different resources, cultures, or systems.
    3. Adapting to local reality: Rather than following fixed steps, complex learning helps people adapt general principles to their specific situation. A rural clinic and an urban hospital might need different approaches even when dealing with the same disease.
    4. Connecting different types of knowledge: Complex learning brings together technical knowledge (facts and procedures) with practical wisdom (experience and judgment). Both are needed to solve real health challenges.
    5. Learning from mistakes: In complex learning, mistakes are valuable opportunities to learn, not failures to be hidden. When something doesn’t work, the question becomes “What can we learn from this?” rather than “Who is to blame?”

    Why it matters for health work:

    Most health challenges are complex problems. Disease outbreaks, vaccination campaigns, and health system improvements all require more than just technical knowledge. They require the ability to:

    • Adapt to changing situations
    • Work with limited resources
    • Coordinate with different groups
    • Solve unexpected problems
    • Learn from experience

    Complex learning builds these abilities by engaging people with real challenges, supporting them as they try solutions, and helping them reflect on what they learn.

    Unlike traditional training that assumes knowledge flows from experts to learners, complex learning recognizes that knowledge emerges through practice and experience. When health workers engage with complex learning, they don’t just know more – they become better problem-solvers capable of addressing the unique challenges in their communities.

  • What is networked learning?

    What is networked learning?

    Networked learning happens when people learn through connections with others facing similar challenges. Think about how market traders learn their business – not through formal classes, but by connecting with other traders, sharing tips, and learning from each other’s experiences. This natural way of learning through relationships is what networked learning tries to support.

    5 key features of networked learning:

    1. Learning from peers: In networked learning, people learn as much or more from others doing similar work as they do from experts. A community health worker in one village might discover an effective way to increase vaccination rates that could help workers in other villages.
    2. Knowledge flows in all directions: Unlike traditional training where knowledge flows only from the top down, networked learning allows knowledge to move in all directions – from national programs to local clinics, between regions, and from local implementers up to policy makers.
    3. Connections create value: The relationships between people become valuable resources for solving problems. Having a network of colleagues to ask for advice or share experiences with helps everyone work more effectively.
    4. Crossing boundaries: Networked learning connects people who might not normally work together – like doctors, nurses, community health workers, and managers. These diverse connections bring together different perspectives and create new solutions.
    5. Building on existing relationships: People already learn from colleagues they trust. Networked learning strengthens these natural connections and creates new ones, expanding who people can learn from.

    Why networked learning matters for health work:

    Health systems are full of isolated practitioners who could benefit from each other’s knowledge:

    • A nurse who developed an effective patient education approach
    • A community health worker who found a way to reach remote households
    • A clinic manager who improved medicine supply systems
    • A doctor who adapted treatment guidelines for local conditions

    Networked learning connects these isolated pockets of knowledge, allowing good ideas to spread and adapt across different contexts.

    Unlike traditional training that pulls people away from their work for workshops, networked learning happens through ongoing connections that support everyday problem-solving. When health workers participate in networked learning, they gain access to a community of practice that continues to provide support long after formal training ends.

    Networked learning doesn’t replace expertise, but it recognizes that valuable knowledge exists throughout the health system – not just at the top. By connecting this distributed knowledge, networked learning helps good practices spread more quickly and adapt more effectively to local needs.