“I understood that if we want to cry, we can cry,” reflected a practitioner in the Certificate peer learning programme on Psychological First Aid (PFA) in support of children affected by the humanitarian crisis in Ukraine – illustrating the kind of personal transformation that complements technical training.
During the ChildHub “Webinar on Psychological First Aid for Children; Supporting the Most Vulnerable”, the Geneva Learning Foundation’s Reda Sadki explained how peer learning provides value that traditional training alone cannot deliver. The EU-funded program on Psychological First Aid (PFA) for children demonstrates that practitioners gain five specific benefits:
First, peer learning reveals contextual wisdom missing from standardized guidance. While technical training provides general principles, practitioners encounter varied situations requiring adaptation. When Serhii Federov helped a frightened girl during rocket strikes by focusing on her teddy bear, he discovered an approach not found in manuals: “This exercise helped the girl switch her focus from the situation around her to caring for the bear.”
Second, practitioners document pattern recognition across diverse cases. Sadki shared how analysis of practitioner experiences revealed that “PFA extends beyond emergency situations into everyday environments” and “children often invent their own therapeutic activities when given space.” These insights help practitioners recognize which approaches work in specific contexts.
Third, peer learning validates experiential knowledge. One practitioner described how simple acknowledgment of feelings often produced visible relief in children, while another found that basic physical comforts had significant psychological impact. These observations, when shared and confirmed across multiple practitioners, build confidence in approaches that might otherwise seem too simple.
Fourth, the network provides real-time problem-solving for urgent challenges. During fortnightly PFA Connect sessions, practitioners discuss immediate issues like “supporting children under three years” or “recognizing severe reactions requiring referrals.” As Sadki explained, these sessions produce concise “key learning points” summarizing practical solutions practitioners can immediately apply.
Finally, peer learning builds professional identity and resilience. “There’s a lot of trust in our network,” Sadki quoted from a participant, demonstrating how sharing experiences reduces isolation and builds a supportive community where practitioners can acknowledge their own emotions and challenges.
The webinar highlighted how this approach creates measurable impact, with practitioners developing case studies that transform tacit knowledge into documented evidence and structured feedback that helps discover blind spots in their practice.
For practitioners interested in joining, Sadki outlined multiple entry points from microlearning modules completed in under an hour to more intensive peer learning exercises, all designed to strengthen support to children while building practitioners’ own professional capabilities.
This project is funded by the European Union. Its contents are the sole responsibility of TGLF, and do not necessarily reflect the views of the European Union.
Throughout the podcast, the hosts explore how the Geneva Learning Foundation (TGLF) has developed a five-step process to improve HPV vaccination implementation through their “Teach to Reach” program. This process involves:
Gathering experiences from health workers worldwide
Analyzing these experiences for patterns and innovative solutions
Conducting deep dives into specific case studies
Bringing national EPI planners into the conversation
Synthesizing and sharing knowledge back with frontline workers
The hosts emphasize that this approach represents a shift from traditional top-down strategies to one that values the collective intelligence of over 16,000 global health workers who implement these programs.
Surprising findings
The AI hosts discuss several findings from peer learning that may seem counterintuitive, including:
Tribal communities often show less vaccine hesitancy than urban populations, potentially due to stronger community ties and trust in traditional leaders
Teachers sometimes have more influence than health workers when it comes to vaccination recommendations
Simple, clear communication is often more effective than complex strategies
Religious institutions can become powerful allies when approached respectfully
Male community leaders can be crucial advocates for what’s typically framed as a women’s health issue
Effective implementation strategies
The hosts highlight various successful implementation approaches mentioned in Sadki’s article:
Cancer survivors serving as powerful advocates
WhatsApp groups connecting community health workers for information sharing
Engaging schoolchildren as messengers to initiate family conversations
Integrating vaccination efforts with existing women’s groups
Community theater and traditional storytelling methods
Less formal settings often producing better results than clinical environments
System-level insights
The podcast discussion reveals that successful vaccination programs don’t necessarily require abundant resources. Instead, key factors include:
Strong leadership and clear vision
Commitment to continuous learning
Community mobilization and trust-building
Leveraging informal networks
Prioritizing social factors over technical ones
Local adaptation rather than standardization
The AI hosts conclude by reflecting on how these principles challenge global health epidemiologists to reconsider their roles—moving beyond data analysis to becoming facilitators who empower communities to develop their own solutions.
This article is based on my presentation 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 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.
The peer learning network alternative
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.
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:
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.
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.
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.
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.
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
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:
Relative lack of learning opportunities
Limited ability to experiment and take risks
Low tolerance for failure
Focus on task completion at the expense of building capacity for future performance
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:
Motivate health workers to believe strongly in the importance of what they do
Give them practice dealing with difficult situations they might face
Build mental resilience for facing obstacles
Prompt them to enlist coworkers for support
Help them engage their bosses to provide guidance, support, and resources
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:
Rethink how we learn
Question how we engage with families and communities
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.
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
Maintain human agency in decision-making
Support rather than replace local leadership
Enhance collective intelligence
Enable rapid learning and adaptation
Preserve context sensitivity
Facilitate knowledge flow across boundaries
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
Dimension
Current State
AI as Tools
AI as Partners
Potential Impact
Knowledge creation
Health workers share observations and experiences through peer networks
AI analyzes patterns in shared data
AI engages in dialogue with health workers, asking questions, suggesting connections, validating insights
New forms of collective intelligence combining human and machine capabilities
Learning process
Structured peer learning through digital platforms and networks
AI delivers content and analyzes performance
AI participates in peer learning networks, contributes insights, supports adaptation
Accelerated learning through human-AI collaboration
Local leadership
Health workers develop and implement solutions for community challenges
AI provides recommendations based on data analysis
AI works alongside local leaders to explore options, model scenarios, validate approaches
Enhanced decision-making combining local wisdom with AI capabilities
Network formation
Lateral connections between health workers across regions
AI matches similar profiles or challenges
AI actively facilitates network development, identifies valuable connections
More effective knowledge networks leveraging both human and machine intelligence
Implementation support
Peer review and structured feedback on action plans
AI checks plans against best practices
AI engages in iterative dialogue about implementation challenges and solutions
Improved implementation through combined human-AI problem-solving
Evidence generation
Documentation of experiences and outcomes through structured processes
AI analyzes implementation data
AI collaborates with health workers to develop and test hypotheses about what works
New approaches to generating practice-based evidence
The International Federation of Red Cross and Red Crescent Societies (IFRC) and The Geneva Learning Foundation (TGLF) are launching PFA Connect, a new platform for education, social work, and health professionals who support children from Ukraine. The platform builds on a new peer learning network launched by IFRC and TGLF in 2024 that is already reaching more than 2,000 practitioners from 27 European countries.
This network responds to a critical need: while traditional training provides essential foundations, professionals benefit most from exchanging practical solutions with peers facing similar challenges. “I felt like I was part of a community of like-minded people who care about children’s mental health,” shares Halyna Fedoryshyn, an education professional from Ukraine who earned her first PFA certificate in 2024. “I had the opportunity to expand my social contacts with professionals outside of Ukraine,” .
“PFA” refers to Psychological first aid (PFA), a practical way to support children experiencing crisis-related distress. This includes creating safe spaces, listening without pressure to talk, addressing immediate needs, and connecting children with appropriate support services. Through PFA Connect, practitioners will share experience to help problem-solve common challenges.
Andreea-Elena Andras, a Red Cross health professional in Romania explains: “By hearing and learning from real stories, I learned new ways of linking with children and create a safe place, such as grounding, breathing and other techniques”.
PFA Connect aims to address a critical need identified through work with practitioners: while training provides essential foundations, professionals build capacity through experience. Exchanging practical solutions with peers facing similar challenges can accelerate the ability to support children from Ukraine.
PFA Connect will offer 30-minute online sessions in English and Ukrainian where practitioners share challenges and solutions. The platform aims to complement existing Red Cross activities by focusing on rapid exchange between professionals.
The initiative operates as part of a broader European Union-funded project through EU4Health programme, involving the Ukrainian Red Cross and 27 other European Red Cross Societies, with the technical support and expertise of the Red Cross Red Crescent (RCRC) Movement MHPSS Hub,
“Throughout 2024, we have witnessed the power of practitioners learning from each other’s experiences,” says Panu Saaristo , Europe’s Regional Manager for Health and Care at the IFRC. “Our collaboration with The Geneva Learning Foundation represents our commitment to strengthen this peer learning approach, recognizing that the most effective solutions often come from professionals working directly with affected children.”
“I feel more equipped to make a positive impact in my role,” reported Jelena Horvat Petanjko, an education professional from Croatia. “The practical knowledge and real-life examples inspired me to adapt my methods and approach challenges with greater empathy and creativity.”
“The challenges facing professionals supporting Ukrainian children cannot be solved through traditional training alone,” explains Reda Sadki Sadki, Executive Director of The Geneva Learning Foundation. “What we have learned is that the solutions already exist within the network of practitioners. Our role is to connect them with each other.”
PFA Connect will rapidly scale and expand this network, providing a rapid way for professionals to tap into the network’s collective intelligence in supporting Ukrainian children.
The network’s growth so far has been driven by Ukrainian professionals, especially those working in fragile contexts.
“Thanks to peer learning that is certified, I am able to provide better quality support and transfer knowledge about it to others,” says Alyona Kryvulyak, a social worker.
“I had answers to my questions… I can use my knowledge in practice… I saw that there are many perspectives,” notes Olga Synytsyna, a social work professional in Ukraine.
“In emergency response, we often focus on training and technical solutions,” says Reda Sadki. “But what we have learned from Ukrainian practitioners is that the most powerful solutions often emerge when professionals can learn directly from each other’s experience.”
For mental health professional Natalia Tsumarieva in Ukraine, peer learning has shifted her approach to supporting Ukrainian children: “I began to pay more attention to providing support in the initial stages of getting to know children. Understanding the importance of teaching these skills to my non-psychology students has also been valuable.”
While driven by those facing the most acute and urgent situations, this has become a truly Europe-wide project. As a Croatian education professional noted, “It is encouraging and inspiring to connect with people across Europe with the same goal and similar experiences. This shows that culture, gender and age are no barrier to mutual understanding and learning about supporting children.”
“Connecting practitioners across borders creates new possibilities,” adds Reda Sadki. “A social worker in Ukraine might develop an innovative approach that could help a teacher in Croatia facing similar challenges. Our role is to make these connections possible at scale.”
Professionals interested in joining the platform can register for the January 29 launch session, which begins at 4:00 PM CET. For additional information and to request your invitation, visit the PFA Connect platform. https://www.learning.foundation/ukraine
Note: This initiative is funded by the European Union through the EU4Health programme. Its contents are the sole responsibility of TGLF and IFRC, and do not necessarily reflect the views of the European Union.
The path to strengthening immunization systems requires innovative technical assistance approaches to learning and capacity building. A recent correspondence in The Lancet proposes peer learning in immunization programmes as a crucial mechanism for achieving the goals of the Immunization Agenda 2030 (IA2030), arguing for “an intentional, well coordinated, fit-for-purpose, data-driven, and government-led immunisation peer-learning plan of action.” This proposal merits careful examination, particularly as immunization programmes face complex challenges in reaching 2030 goals.
The Lancet commentary identifies several key rationales for peer learning in immunization.
First, “immunisation policy makers operate in dynamic sociopolitical and economic contexts that often compel quick decision making.” In such environments, peer knowledge becomes crucial “when research evidence is scarce.”
Second, the authors recognize that “contextual factors in immunisation systems are constantly interacting to exhibit emergent behaviour and self-organisation,” necessitating constant adaptation of technical approaches.
These insights point toward an important truth: traditional approaches to knowledge sharing – whether through technical guidelines, formal training, or policy exchange – remain necessary but increasingly insufficient for today’s challenges.
The question becomes not just how to share what we know, but how to systematically generate new knowledge about what works in different contexts.
Complementary approaches to peer learning in immunization programmes
While government counterparts learning from each other offers valuable benefits for policy coordination and strategic alignment, implementation challenges are situated – and solved – at the local levels. This call for complementary peer learning approaches. Three stand out as particularly critical:
First, the persistent gap between national planning and local implementation suggests the need for systematic learning about how policies and strategies are turned into effective, community-led and -owned action on the ground.
Second, as programmes work to sustain coverage gains beyond campaign-based interventions, they need reliable mechanisms for identifying and spreading effective practices for routine immunization.
Third, the continuous influx of new staff into EPI teams creates an ongoing need for rapid capacity building that goes beyond technical training to include development of professional networks and practical implementation skills.
From reporting challenges to creating implementation knowledge
A crucial distinction emerges between simply documenting implementation challenges and systematically creating new knowledge about effective implementation. This difference parallels the distinction in epidemiology between case reporting and analytical epidemiology.
When health workers report challenges, they might note that coverage is low in remote areas due to transport limitations, staff shortages, and cold chain issues. This provides valuable surveillance data but does not necessarily generate actionable knowledge. In contrast, systematic analysis of successful remote area coverage can reveal specific transport solutions that work, staff deployment patterns that succeed, and cold chain adaptations that enable reach.
This shift from reporting to knowledge creation requires careful structure and support. Just as analytical epidemiology employs specific methods to move from observation to insight, systematic peer learning needs frameworks and processes that enable pattern recognition, cross-context learning, and theory building about what works.
Enabling systematic learning at scale
Recent experience demonstrates the feasibility of systematic peer learning at scale. For example, Gavi-supported country-led initiatives facilitated by The Geneva Learning Foundation (TGLF) in Côte d’Ivoire and Nigeria, health workers from districts and facilities shared specific strategies through structured processes, they collectively generate new knowledge about effective implementation. Launched in 2022 with support from Wellcome, the Movement for Immunization Agenda 2030 (IA2030) has demonstrated that such ground-level learning, when properly captured and analyzed, provides crucial insights for national planning.
Consider the introduction of new vaccines. When thousands of practitioners share specific experiences about what enables successful introduction, patterns emerge that might be missed in smaller exchanges or formal evaluations. These patterns help reveal not just what works, but how solutions adapt and evolve across contexts.
Supporting new EPI staff through networked learning
The challenge of rapidly building capacity when new staff join EPI teams highlights the potential value of structured peer learning. Training approaches like Mid-Level Management (MLM) Training provide essential technical foundations, and have been able to reach more professionals by moving online. However, new staff also need to rapidly build professional networks and learn from peers facing similar challenges.
A cohort-based approach combining technical training with structured peer learning can accelerate both capability development and network formation. This helps new staff analyze local challenges, identify priorities, and access peer support for implementation. Cross-country learning opportunities are particularly valuable for young professionals, enabling them to build relationships beyond hierarchical constraints.
From vaccination campaigns to sustainable primary health care systems that integrate routine immunization
For immunization programmes work to sustain coverage gains beyond campaign-based interventions, peer learning networks are needed to support the transition to stronger routine immunization systems. By connecting practitioners across health system levels, these networks help identify and spread effective practices for reaching families through regular services.
This network-based approach complements formal exchange mechanisms by creating multiple pathways for knowledge flow:
Ground-level innovations inform national strategy through systematic capture and analysis
Peer feedback helps practitioners adapt solutions to local contexts
Implementation experiences create evidence about what works and why
Cross-level dialogue strengthens connections between policy and practice
Peer learning embedded into government-owned health systems
This peer learning approach does not replace traditional technical assistance, capacity building, or policy exchange. Rather, it transforms them by creating new connections between levels and actors in health systems. While formal exchanges remain crucial for policy coordination, structured peer learning adds vital capabilities:
Granular understanding of implementation challenges while maintaining systematic rigor in knowledge capture;
Documentation of practical innovations while creating frameworks for adaptation across contexts; and
Evidence-based feedback loops between policy and practice.
Success requires careful attention to structure. Through carefully designed processes, practitioners engage in cycles of sharing, feedback, connection, and action. This structure is not bureaucratic control but scaffolding that supports genuine knowledge creation and application.
Looking forward
The World Health Organization’s Executive Board has highlighted widening inequities between and within countries as a critical challenge for immunization programmes. In the African region particularly, where many countries are introducing new vaccines while working to strengthen basic immunization services, innovative approaches are needed.
New evidence from recent large-scale peer learning initiatives suggests that structured approaches can help bridge the gap between strategy and implementation while strengthening both. Success requires investment in learning processes and support structures – but the potential rewards, in terms of accelerated progress and improved outcomes, make this investment worthwhile.
This offers a concrete path toward what WHO calls for: “grounding action in local realities.” By systematically connecting learning across health system levels while maintaining rigorous standards for evidence and implementation support, we can create learning systems that effectively link regional strategy with local innovation and action.
The future of immunization capacity building lies not in choosing between formal exchanges and practitioner networks, but in thoughtfully combining them to create comprehensive learning systems. These systems can drive rapid improvement while strengthening health systems as a whole – an essential goal as we work toward ambitious immunization targets for 2030 and beyond.
Reference
Adamu AA, Ndwandwe D, Jalo RI, Ndoutabe M, Wiysonge CS. Peer learning in immunisation programmes. The Lancet [Internet]. 2024 Jul; 404(10450):334–5. Available from: https://doi.org/10.1016/S0140-6736(24)01340-0
Jones, I., Sadki, R., Brooks, A., Gasse, F., Mbuh, C., Zha, M., Steed, I., Sequeira, J., Churchill, S., & Kovanovic, V. (2022). IA2030 Movement Year 1 report. Consultative engagement through a digitally enabled peer learning platform (1.0). The Geneva Learning Foundation. https://doi.org/10.5281/zenodo.7119648
This experimental podcast, created in collaboration with generative AI, demonstrates a novel approach to exploring complex learning concepts through a conversational framework that is intended to support dialogic learning. Based on TGLF’s 2024 end-of-year message and supplementary materials, the conversation examines their peer learning model through a combination of concrete examples and theoretical reflection. The dialogue format enables exploration of how knowledge emerges through structured interaction, even in AI-generated content.
Experimental nature and limitations of generative AI for dialogic learning
This content is being shared as an exploration of how generative AI might contribute to learning and knowledge construction. While based on TGLF’s actual 2024 message, the dialogue includes AI-generated elaborations that may contain inaccuracies. However, these limitations themselves provide interesting insights into how knowledge emerges through interaction, even in artificial contexts.
You can read our actual 2024 Year in review message here.
Pedagogical value and theoretical implications of a generative AI conversational framework
Structured knowledge construction: The conversational framework illustrates how knowledge can emerge through structured dialogue, even when artificially generated. This mirrors TGLF’s own insights about how structure enables rather than constrains dialogic learning.
Multi-level learning: The dialogue operates on multiple levels:
Direct information sharing about TGLF’s work
Modeling of reflective dialogue
Meta-level exploration of how knowledge emerges through interaction
Integration of concrete examples with theoretical reflection
Network effects in learning: The conversation demonstrates how different types of knowledge (statistical, narrative, theoretical, practical) can be woven together through dialogue to create deeper understanding. This parallels TGLF’s observations about how learning emerges through structured networks of interaction.
We invite listeners to consider:
How a conversational framework enables exploration of complex ideas
The role of structure in enabling knowledge emergence
The relationship between concrete examples and theoretical understanding
The potential and limitations of AI in supporting dialogic learning
This experiment invites reflection not just on the content itself, but on how knowledge and understanding emerge through structured interaction – whether human or artificial.
Your insights about how this generative AI format affects your understanding will help inform future explorations of AI’s role in learning.
What aspects of the conversational framework enhanced or hindered your understanding?
How did the interplay of concrete examples and reflective discussion affect your learning?
What difference did it make that you knew before listening that the conversation was created using generative AI?
We welcome your thoughts on these deeper questions about how learning happens through structured interaction.
This World Bank report ‘The Cost of Inaction: Quantifying the Impact of Climate Change on Health in Low- and Middle-Income Countries’ presents new analysis of climate change impacts on health systems and outcomes in the regions that are bearing the brunt of these impacts.
Key analytical insights to quantify climate change impacts on health
The report makes three contributions to our understanding of climate-health interactions:
First, it quantifies the massive scale of climate change impacts on health, projecting 4.1-5.2 billion climate-related disease cases and 14.5-15.6 million deaths in LMICs by 2050. This represents a significant advancement over previous estimates, which the report demonstrates were substantial underestimates.
Second, it illuminates the profound economic consequences, calculating costs of $8.6-20.8 trillion by 2050 (0.7-1.3% of LMIC GDP). The report employs both Value of Statistical Life and Years of Life Lost approaches to provide a range of economic impact estimates.
Third, it reveals stark geographic inequities in impact distribution, with Sub-Saharan Africa bearing approximately 71% of cases and nearly half of deaths, while South Asia faces about 18% of cases and a quarter of deaths. This spatial analysis helps identify where interventions are most urgently needed.
Policy implications and systemic perspectives
The report’s findings point to several critical policy directions:
The need for systemic rather than disease-specific interventions emerges as a central theme. The authors explicitly advocate for strengthening entire health systems rather than pursuing vertical disease programs.
The economic analysis makes a compelling case for immediate action, demonstrating that the costs of inaction far exceed potential investment requirements for climate-resilient health systems.
The geographic distribution of impacts highlights the need for globally coordinated responses while prioritizing support for the most vulnerable regions.
The findings suggest that transforming systems to address climate change impacts on health requires not just technical solutions but fundamental rethinking of how health systems are organized and financed in vulnerable regions.
The report’s emphasis on systemic approaches represents a significant shift in thinking about climate-health interventions. This merits unpacking on several levels:
Inadequacy of vertical disease silos: The report challenges the traditional vertical disease management paradigm that has dominated global health programming for decades. While vertical programs have achieved notable successes in areas like HIV/AIDS or malaria control, the report argues that climate change’s multifaceted health impacts require a fundamentally different approach.
Need for systemic intervention: Climate change simultaneously affects multiple disease pathways, nutrition status, and health infrastructure. These interactions cannot be effectively addressed through isolated disease-specific programs. Building core health system capabilities (surveillance, emergency response, primary care) creates multiplicative benefits across various climate-related health challenges. Strong health systems can better identify and respond to emerging threats, whereas vertical programs often lack this flexibility.
Implementation implications: The report suggests this systemic approach requires: integrated planning across health system components, flexible funding mechanisms that support system-wide capabilities, enhanced coordination between different health programmes and investment in cross-cutting infrastructure and capabilities.
What about the health workforce facing impacts of climate change on health?
Between this clear-eyed assessment and effective action lies a critical implementation gap.
Interestingly, the report gives limited explicit attention to the health workforce dimension of climate-health challenges. Yet that is precisely where we need to focus attention, given that:
Health workers based in communities are first responders to climate-related health emergencies
Workforce capacity significantly determines a health system’s adaptive capabilities
Climate change itself affects health worker distribution and effectiveness
Given the report’s emphasis on systemic approaches, the lack of detailed discussion about human resources for health represents a missed opportunity to explore what effective action might look like.
The Geneva Learning Foundation’s network, developed through nearly a decade of research and practice, has led us to identify a path for supporting the health workforce to strengthen preparedness and response in response to climate change impacts on health.
The network already connects over 60,000 health workers. They represent all job roles, rank, and levels of the health system.
One distinguishing feature of this network is its deep integration with existing government health systems. Over half of network participants are government employees, from community health workers to district officers to national planners.
62% of participants work in remote rural areas, 47% serve urban poor populations, and 21% operate in conflict zones.
These are not just statistics: they represent an unprecedented capability to mobilize knowledge and action where it’s most needed.
Since 2023, network participants have been sharing observations, experiences, and insights of climate change impacts on health.
The model connects different levels of health systems:
Community-based health workers share ground-level observations
District managers identify emerging patterns
National planners gauge system-wide implications
Global partners access real-time insights
When a malaria control officer in Kenya observes changing disease patterns due to altered rainfall, the network enables rapid sharing of this insight with colleagues working on water safety, nutrition, and primary care. These cross-domain connections do not need to be left to chance – they can be enabled through structured peer learning processes that transcend traditional programme, geographic, and hierarchical boundaries
This creates what organizational theorists call “embedded transformation” – where system change emerges through existing structures rather than requiring new ones.
Rather than creating new coordination mechanisms, the network enables:
Health workers to learn directly from peers in other programs
Rapid identification of cross-cutting challenges
Spontaneous formation of problem-solving groups
Systematic sharing of effective practices
Rather than replacing existing structures, TGLF’s model demonstrates how digital networks can enable health systems to:
Maintain necessary specialization while fostering crucial connections
Enable rapid learning and adaptation across programs
Optimize resource use through enhanced coordination
Build system-wide resilience through structured peer learning
Such a network enables what complexity theorists call “distributed sensing” that can provide:
Early warning of emerging threats
Rapid sharing of local solutions
System-wide learning from local innovations
Continuous adaptation to changing conditions
This has led us to posit that investment in such emergent digital networks could enable health systems to maintain necessary specialization while fostering crucial connections across domains.
This is obviously critical to respond to the systems-level complexity of climate change impacts on health.
World Bank finding
TGLF model strategic fit
Scale of impact (4.1-5.2B cases, 14.5-15.6M deaths by 2050)
TGLF’s digital network model demonstrates scalability, already connecting over 60,000 health practitioners across 137 countries. More significantly, the model’s effectiveness increases with scale – as more practitioners join, the network’s ability to identify emerging threats and disseminate effective responses improves. Network analysis shows that larger scale enables more diverse inputs and faster adaptation, suggesting this approach could help health systems respond to the massive scale of projected impacts.
Economic consequences ($8.6-20.8T by 2050)
TGLF’s model offers remarkable cost-effectiveness through its networked learning structure. Rather than requiring massive new investments in parallel systems, it leverages existing health system resources while enabling and accelerating both learning and action. The model demonstrates how digital infrastructure can maximize return on investment – practitioners implement solutions using existing resources, with 82% reporting ability to continue without external support. This suggests potential for significant cost savings while building system resilience.
Geographic inequities (71% SSA, 18% SA)
TGLF’s network already demonstrates strongest presence precisely where the World Bank identifies greatest need – 70% of participants work in Sub-Saharan Africa and South Asia. This concentration is not coincidental; the model’s digital infrastructure and peer learning approach prove particularly effective in resource-constrained settings. The network enables rapid sharing of context-appropriate solutions between regions facing similar challenges, while maintaining sensitivity to local conditions.
Need for systemic intervention
The network transcends traditional program boundaries through what organizational theorists call “structured emergence” – practitioners naturally form cross-program connections based on shared challenges. When a malaria control officer observes changing disease patterns due to climate shifts, the network enables rapid sharing with colleagues in water safety, nutrition, and primary care. This organic integration emerges through peer learning rather than requiring new coordination mechanisms.
Urgency of investment
TGLF’s model offers an immediately scalable approach that builds on existing health system capabilities. Rather than waiting years to develop new infrastructure, the network can rapidly expand to connect more practitioners and regions. Evidence shows 7x acceleration in implementation of new approaches compared to conventional means of technical assistance, suggesting potential for rapid, sustainable strengthening of health system resilience.
Global coordination need
While enabling global connection, the network maintains strong local grounding through its emphasis on locally-led action and contextual adaptation. Government health workers comprise over 50% of participants, creating what scholars term “embedded transformation” – change emerging through existing structures rather than imposed from outside. This enables coordinated response while respecting local health system authority.
System transformation
The model demonstrates how digital networks can fundamentally transform how health systems operate without requiring complete restructuring. By enabling rapid knowledge flow across traditional boundaries, supporting emergence of new coordination patterns, and fostering system-wide learning, it shows how transformation can emerge through enhanced connection rather than structural overhaul. Analysis reveals development of new capabilities in surveillance, response, and adaptation through networked learning.
Reference
Uribe, J.P., Rabie, T., 2024. The Cost of Inaction: Quantifying the Impact of Climate Change on Health in Low- and Middle-Income Countries. The World Bank, Washington, D.C. https://doi.org/10.1596/42419
The gap between theoretical knowledge and practical implementation remains one of the most persistent challenges in global health. This divide manifests in multiple ways: research that fails to address practitioners’ urgent needs, innovations from the field that never inform formal evidence systems, and capacity building approaches that cannot meet the massive scale of learning required. Donald Schön’s seminal 1995 analysis of the “dilemma of rigor or relevance” in professional practice offers crucial insights for “knowing-in-action“. It can help us understand why transforming global health requires new ways of knowing – a new epistemology.
Listen to this article below. Subscribe to The Geneva Learning Foundation’s podcast for more audio content.
Schön’s analysis: The dilemma of rigor or relevance
Schön begins by examining how knowledge becomes institutionalized through education. Using elementary school mathematics as an example, he describes how knowledge is broken into discrete units (“math facts”), organized into progressive modules, assembled into curricula, and measured through standardized tests. This systematization shapes not just content but the entire organization of time, space, and institutional arrangements.
From this foundation, Schön introduces his central metaphor of two contrasting landscapes in professional practice that prevent “knowing-in-action”. As he describes it:
“In the varied topography of professional practice, there is a high, hard ground overlooking a swamp. On the high ground, manageable problems lend themselves to solution through the use of research-based theory and technique. In the swampy lowlands, problems are messy and confusing and incapable of technical solution.”
The cruel irony, Schön observes, lies in the relative importance of these terrains: “The problems of the high ground tend to be relatively unimportant to individuals or to society at large, however great their technical interest may be, while in the swamp lie the problems of greatest human concern.”
This creates what Schön calls the “dilemma of rigor or relevance” – practitioners must choose between remaining on the high ground where they can maintain technical rigor or descending into the swamp where they must rely on experience, intuition, and what he terms “muddling through.”
The historical roots of the divide
Schön traces this dilemma to the epistemology embedded in modern research universities. Drawing on Edward Shils’s historical analysis, he describes how American scholars returning from Germany after the Civil War brought back “the German idea of the university as a place in which to do research that contributes to fundamental knowledge, preferably through science.”
This was, as Schön notes, “a very strange idea in 1870,” running counter to the prevailing British model of universities as sanctuaries for liberal arts or finishing schools for gentlemen. The new model first took root at Johns Hopkins University, whose president embraced the “bizarre notion that professors should be recruited, promoted, and granted tenure on the basis of their contributions to fundamental knowledge.”
This shift created what Schön terms the “Veblenian bargain” (named after Thorstein Veblen), establishing a separation between:
Research universities focused on “true scholarship” and fundamental knowledge
Professional schools dedicated to practical training
Knowing-in-action in global health: From fragmentation to integration
The historical division between theory and practice that Schön identified continues to shape global health in profound and often problematic ways. This manifests in three interconnected challenges that demand our urgent attention: the knowledge-practice gap, the scale challenge, and the complexity challenge. Yet emerging approaches suggest potential paths forward, particularly through structured peer learning networks that could help bridge Schön’s “high ground” and “swamp.”
The separation between research institutions and field practice creates not just an academic concern but a practical crisis in healthcare delivery. Consider the response to COVID-19: while research institutions rapidly generated new knowledge about the virus, frontline health workers struggled to translate this into practical approaches for their specific contexts. Their hard-won insights about what worked in different settings rarely made it back into formal evidence systems, epitomizing the one-way flow of knowledge that impoverishes both research and practice.
This pattern repeats across global health. Research agendas, shaped by academic incentives and funding priorities, often fail to address practitioners’ most pressing challenges. A community health worker in rural Bangladesh facing complex challenges around vaccine hesitancy may struggle to find relevant guidance – while global experts are convinced that they already have all the answers. Meanwhile, local solutions to building vaccine confidence remain uncaptured by formal knowledge systems.
The rise of implementation science attempts to bridge this divide, yet often remains subordinate to “pure” research in academic hierarchies. This reflects Schön’s observation about the privileging of high ground problems over swampy ones, even when the latter hold greater practical significance.
Traditional approaches to professional education face fundamental limitations in meeting the massive need for health worker capacity building. The World Health Organization projects a shortfall of 10 million health workers by 2030, mostly in low- and middle-income countries. Conventional training approaches that rely on cascading knowledge through workshops and formal courses can reach only a fraction of those who need support.
More fundamentally, these knowledge transmission models prove inadequate for addressing complex local realities. A standardized curriculum developed by experts, no matter how well-designed, cannot anticipate the diverse challenges health workers face across different contexts. When a district immunization manager in Nigeria must adapt vaccination strategies for nomadic populations during a drought, they need more than pre-packaged knowledge – they need ways to learn from others who are facing similar challenges.
Resource constraints further limit the reach of conventional approaches. The cost of traditional training programmes, both in money and time away from service delivery, makes it impossible to scale them to meet the need. Yet the human cost of this capacity gap, measured in preventable illness and death, demands urgent solutions.
Similarly, emerging and re-emerging infectious diseases demand responses that cross traditional boundaries between animal and human health, environmental factors, and social determinants. Health workforce development must grapple with complex systemic issues around motivation, retention, and capacity building. The COVID-19 pandemic demonstrated how traditional approaches to health system strengthening often prove inadequate in the face of complex adaptive challenges.
Emerging solutions: A new paradigm for learning and practice
Recent innovations suggest promising approaches to bridging these divides through structured peer learning networks. Digital platforms enable health workers to share experiences and solutions across geographical boundaries, creating new possibilities for scaled learning that maintains local relevance.
Solution #1: The power of structured peer learning
Experience from digital learning networks demonstrates how structured peer interaction can enable more efficient and effective knowledge sharing than traditional top-down approaches. When health workers can directly connect with peers facing similar challenges, they not only share solutions but collectively generate new knowledge through their interactions.
These networks provide mechanisms for validating practical knowledge through peer review processes that complement traditional academic validation. A successful intervention developed by a rural clinic in Thailand can be critically examined by peers, adapted for different contexts, and rapidly disseminated across the network. This creates a more dynamic and responsive knowledge ecosystem than traditional publication cycles allow.
Solution #2: Network effects and collective intelligence
The potential of practitioner networks extends beyond simple knowledge sharing. When properly structured, these networks create possibilities for:
Rapid adaptation to emerging challenges through real-time sharing of experiences
Development of context-specific solutions that build on shared learning
Most importantly, these networks can help bridge Schön’s high ground and swamp by creating dialogue between different forms of knowledge and practice. They provide spaces where academic research can inform field practice while simultaneously allowing field insights to shape research agendas.
Four principles toward knowing-in-action for global health
Drawing on Schön’s call for a “new epistemology,” we can identify four principles for transforming how we know what we know in global health:
The complexity of contemporary health challenges demands recognition of multiple valid forms of knowledge. The practical wisdom developed by a community health worker through years of service deserves attention alongside randomized controlled trials. This requires challenging existing hierarchies of evidence while maintaining rigorous standards for validating knowledge claims.
Principle #2: Enabling knowledge creation from practice
Health workers must be supported as knowledge producers, not just knowledge consumers. This means creating structures for systematically capturing and validating field insights, building evidence from implementation experience, and enabling continuous learning from practice. Digital platforms can provide scaffolding for this knowledge creation while ensuring quality through peer review processes.
Traditional scaling approaches that rely on standardization and top-down dissemination must be complemented by networked learning to create and amplify knowing-in-action. This means building systems that can:
Connect practitioners across contexts and boundaries
Enable peer validation of knowledge
Support rapid dissemination of innovations
Build collective intelligence through structured interaction
Rather than seeking to reduce complexity through standardization, health systems must build capacity for working effectively within complex adaptive systems. This means supporting adaptive learning, enabling context-specific solutions, and building capacity for systems thinking at all levels.
The challenges facing global health today demand new ways of creating, validating, and sharing knowledge. By embracing approaches that bridge Schön’s high ground and swamp, we may find paths toward health systems that are both more rigorous and more relevant to the communities they serve.
Looking forward
Schön’s analysis helps explain why traditional approaches to global health knowledge and learning often fall short. More importantly, it points toward solutions that could help bridge the theory-practice divide to support knowing-in-action:
New digital platforms that enable peer learning at scale
Networks that connect practitioners across contexts
Approaches that validate practical knowledge
Systems that support rapid learning and adaptation
Schön’s insights remain remarkably relevant to contemporary global health challenges. His call for a new epistemology that can bridge theory and practice speaks directly to our current needs. By embracing new approaches to learning and knowledge creation that honor both rigor and relevance, we may find ways to address the complex challenges that lie ahead.
The key lies not in choosing between high ground and swamp, but in building new kinds of bridges between them – bridges that can support the massive scale of learning needed while maintaining the local relevance essential for impact. Recent innovations in peer learning networks and digital platforms suggest this bridging may be increasingly possible, offering hope for more effective global health practice in an increasingly complex world.
The challenge now is to develop and implement these bridging approaches at the scale needed to support global health workers worldwide. This will require new ways of thinking about knowledge, learning, and practice – ways that honor both the rigor of research and the wisdom of experience. The future of global health may depend on our success in this endeavor.
Listen to the AI podcast deep dive about this article
Reference
Schön, Donald A., 1995. Knowing-in-action: The new scholarship requires a new epistemology. Change: The Magazine of Higher Learning 27, 27–34. https://doi.org/10.1080/00091383.1995.10544673
Global health continues to grapple with a persistent tension between standardized, evidence-based interventions developed by international experts and the contextual, experiential local knowledge held by local health workers. This dichotomy – between global expertise and local knowledge – has become increasingly problematic as health systems face unprecedented complexity in addressing challenges from climate change to emerging diseases.
The limitations of current approaches
The dominant approach privileges global technical expertise, viewing local knowledge primarily through the lens of “implementation barriers” to be overcome. This framework assumes that if only local practitioners would correctly apply global guidance, health outcomes would improve.
This assumption falls short in several critical ways:
It fails to recognize that local health workers often possess sophisticated understanding of how interventions need to be adapted to work in their contexts.
It overlooks the way that local knowledge, built through direct experience with communities, often anticipates problems that global guidance has yet to address.
It perpetuates power dynamics that systematically devalue knowledge generated outside academic and global health institutions.
The hidden costs of privileging global expertise
When we examine actual practice, we find that privileging global over local knowledge can actively harm health system performance:
It creates a “capability trap” where local health workers become dependent on external expertise rather than developing their own problem-solving capabilities.
It leads to the implementation of standardized solutions that may not address the real needs of communities.
Recent experiences from the COVID-19 pandemic provide compelling evidence for the importance of local knowledge. While global guidance struggled to keep pace with evolving challenges, local health workers had to figure out how to keep health services going:
Community health workers in rural areas adapted strategies.
District health teams created new approaches to maintain essential services during lockdowns.
Facility staff developed creative solutions to manage PPE shortages.
These innovations emerged not from global technical assistance, but from local practitioners applying their deep understanding of community needs and system constraints, and by exploring new ways to connect with each other and contribute to global knowledge.
Towards a new synthesis
Rather than choosing between global and local knowledge, we need a new synthesis that recognizes their complementary strengths. This requires three fundamental shifts:
1. Reframing local knowledge
Moving from viewing local knowledge as merely contextual to seeing it as a source of innovation.
Recognizing frontline health workers as knowledge creators, not just knowledge recipients.
Moving beyond the false dichotomy between global and local knowledge opens new possibilities for strengthening health systems. By recognizing and valuing both forms of knowledge, we can create more effective, resilient, and equitable health systems.
The challenges facing health systems are too complex for any single source of knowledge to address alone. Only by bringing together global expertise and local knowledge can we develop the solutions needed to improve health outcomes for all.
References
Braithwaite, J., Churruca, K., Long, J.C., Ellis, L.A., Herkes, J., 2018. When complexity science meets implementation science: a theoretical and empirical analysis of systems change. BMC Med 16, 63. https://doi.org/10.1186/s12916-018-1057-z
Farsalinos, K., Poulas, K., Kouretas, D., Vantarakis, A., Leotsinidis, M., Kouvelas, D., Docea, A.O., Kostoff, R., Gerotziafas, G.T., Antoniou, M.N., Polosa, R., Barbouni, A., Yiakoumaki, V., Giannouchos, T.V., Bagos, P.G., Lazopoulos, G., Izotov, B.N., Tutelyan, V.A., Aschner, M., Hartung, T., Wallace, H.M., Carvalho, F., Domingo, J.L., Tsatsakis, A., 2021. Improved strategies to counter the COVID-19 pandemic: Lockdowns vs. primary and community healthcare. Toxicology Reports 8, 1–9. https://doi.org/10.1016/j.toxrep.2020.12.001
Jerneck, A., Olsson, L., 2011. Breaking out of sustainability impasses: How to apply frame analysis, reframing and transition theory to global health challenges. Environmental Innovation and Societal Transitions 1, 255–271. https://doi.org/10.1016/j.eist.2011.10.005
Salve, S., Raven, J., Das, P., Srinivasan, S., Khaled, A., Hayee, M., Olisenekwu, G., Gooding, K., 2023. Community health workers and Covid-19: Cross-country evidence on their roles, experiences, challenges and adaptive strategies. PLOS Glob Public Health 3, e0001447. https://doi.org/10.1371/journal.pgph.0001447
Yamey, G., 2012. What are the barriers to scaling up health interventions in low and middle income countries? A qualitative study of academic leaders in implementation science. Global Health 8, 11. https://doi.org/10.1186/1744-8603-8-11
