Complex problems

What is a complex problem?

Global health

What is a complex problem and what do we need to tackle it?

Problems can be simple or complex.

Simple problems have a clear first step, a known answer, and steps you can follow to get the answer.

Complex problems do not have a single right answer.

They have many possible answers or no answer at all.

What makes complex problems really hard is that they can change over time.

They have lots of different pieces that connect in unexpected ways.

When you try to solve them, one piece changes another piece, which changes another piece.

It is hard to see all the effects of your actions.

When you do something to help, later on the problem might get worse anyway.

You have to keep adapting your ideas.

To solve really hard problems, you need to be able to:

  • Think about all the puzzle pieces and how they fit, even when you don’t know what they all are.
  • Come up with plans and change them when parts of the problem change.
  • Think back on your problem solving to get better for next time.

The most important things are being flexible, watching how every change affects other things, and learning from experience.

Image: The Geneva Learning Foundation Collection © 2024

References

Buchanan, R., 1992. Wicked problems in design thinking. Design issues 5–21.

Camillus, J.C., 2008. Strategy as a wicked problem. Harvard business review 86, 98.

Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., Siemens, G., 2023. Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education: Artificial Intelligence 4, 100138. https://doi.org/10.1016/j.caeai.2023.100138

Rittel, H.W., Webber, M.M., 1973. Dilemmas in a general theory of planning. Policy sciences 4, 155–169.

Artificial intelligence, accountability, and authenticity knowledge production and power in global health crisis

Artificial intelligence, accountability, and authenticity: knowledge production and power in global health crisis

Global health

I know and appreciate Joseph, a Kenyan health leader from Murang’a County, for years of diligent leadership and contributions as a Scholar of The Geneva Learning Foundation (TGLF). Recently, he began submitting AI-generated responses to Teach to Reach Questions that were meant to elicit narratives grounded in his personal experience.

Seemingly unrelated to this, OpenAI just announced plans for specialized AI agents—autonomous systems designed to perform complex cognitive tasks—with pricing ranging from $2,000 monthly for a “high-income knowledge worker” equivalent to $20,000 monthly for “PhD-level” research capabilities.

This is happening at a time when traditional funding structures in global health, development, and humanitarian response face unprecedented volatility.

These developments intersect around fundamental questions of knowledge economics, authenticity, and power in global health contexts.

I want to explore three questions:

  • What happens when health professionals in resource-constrained settings experiment with AI technologies within accountability systems that often penalize innovation?
  • How might systems claiming to replicate human knowledge work transform the economics and ethics of knowledge production?
  • And how should we navigate the tensions between technological adoption and authentic knowledge creation?

Artificial intelligence within punitive accountability structures of global health

For years, Joseph had shared thoughtful, context-rich contributions based on his direct experiences. All of a sudden, he was submitting generic mush with all the trappings of bad generative AI content.

Should we interpret this as disengagement from peer learning?

Given his history of diligence and commitment, I could not dismiss his exploration of AI tools as diminished engagement. Instead, I understood it as an attempt to incorporate new capabilities into his professional repertoire. This was confirmed when I got to chat with him on a WhatsApp call.

Our current Teach to Reach Questions system has not yet incorporated the use of AI. Our “old” system did not provide any way for Joseph to communicate what he was exploring.

Hence, the quality limitations in AI-generated narratives highlight not ethical failings but a developmental process requiring support rather than judgment.

But what does this look like when situated within global health accountability structures?

Health workers frequently operate within highly punitive systems where performance evaluation directly impacts funding decisions. International donors maintain extensive surveillance of program implementation, creating environments where experimentation carries significant risk. When knowledge sharing becomes entangled with performance evaluation, the incentives for transparency about AI “co-working” (i.e., collaboration between human and AI in work) diminish dramatically.

Seen through this lens, the question becomes not whether to prohibit AI-generated contributions but how to create environments where practitioners can explore technological capabilities without fear that disclosure will lead to automatic devaluation of their knowledge, regardless of its substantive quality. This heavily depends on the learning culture, which remains largely ignored or dismissed in global health.

The transparency paradox: disclosure and devaluation of artificial intelligence in global health

This case illustrates what might be called the “transparency paradox”—when disclosure or recognition of AI contribution triggers automatic devaluation regardless of substantive quality. Current attitudes create a problematic binary: acknowledge AI assistance and have contributions dismissed regardless of quality, or withhold disclosure and risk accusations of misrepresentation or worse.

This paradox creates perverse incentives against transparency, particularly in contexts where knowledge production undergoes intensive evaluation linked to resource allocation. The global health sector’s evaluation systems often emphasize compliance over innovation, creating additional barriers to technological experimentation. When every submission potentially affects funding decisions, incentives for technological experimentation become entangled with accountability pressures.

This dynamic particularly affects practitioners in Global South contexts, who face more intense scrutiny while having less institutional protection for experimentation. The punitive nature of global health accountability systems deserves particular emphasis. Health workers operate within hierarchical structures where performance is consistently monitored by both national governments and international donors. Surveillance extends from quantitative indicators to qualitative assessments of knowledge and practice.

In environments where funding depends on demonstrating certain types of knowledge or outcomes, the incentive to leverage artificial intelligence in global health may conflict with values of authenticity and transparency. This surveillance culture creates uniquely challenging conditions for technological experimentation. When performance evaluation drives resource allocation decisions, health workers face considerable risk in acknowledging technological assistance—even as they face pressure to incorporate emerging technologies into their practice.

The economics of knowledge in global health contexts

OpenAI’s announced “agents” represent a substantial evolution beyond simple chatbots or language models. If they are able to deliver what they just announced, these specialized systems would autonomously perform complex tasks simulating the cognitive work of highly-skilled professionals. The most expensive tier, priced at $20,000 monthly, purportedly offers “PhD-level” research capabilities, working continuously without the limitations of human scheduling or attention.

These claims, while unproven, suggest a potential future where knowledge work economics fundamentally change. For global health organizations operating in Geneva, where even a basic intern position for a recent master’s degree graduate cost more than 200 times that of a ChatGPT subscription, the economic proposition of systems working 24/7 for potentially comparable costs merits careful examination.

However, the global health sector has historically operated with significant labor stratification, where personnel in Global North institutions command substantially higher compensation than those working in Global South contexts. Local health workers often provide critical knowledge at compensation rates far below those of international consultants or staff at Northern institutions. This creates a different economic equation than suggested by Geneva-based comparisons. Many organizations have long relied on substantially lower local labor costs, often justified through capacity-building narratives that mask underlying power asymmetries.

Given this history, the risk that artificial intelligence in global health would replace local knowledge workers might initially appear questionable. Furthermore, the sector has demonstrated considerable resistance to technological adoption, particularly when it might disrupt established operational patterns. However, this analysis overlooks how economic pressures interact with technological change during periods of significant disruption.

The recent decisions of many government to donors to suddenly and drastically cut funding and shut down programs illustrates how rapidly even established funding structures can collapse. In such environments, organizations face existential questions about maintaining operational capacity, potentially creating conditions where technological substitution becomes more attractive despite institutional resistance.

A new AI divide

ChatGPT and other generative AI tools were initially “geo-locked”, making them more difficult to access from outside Europe and North America.

Now, the stratified pricing structure of OpenAI’s announced agents raises profound equity concerns. With the most sophisticated capabilities reserved for those able to pay high costs for the most capable agents, we face the potential emergence of an “AI divide” that threatens to reinforce existing knowledge power imbalances.

This divide presents particular challenges for global health organizations working across diverse contexts. If advanced AI capabilities remain the exclusive province of Northern institutions while Southern partners operate with limited or no AI augmentation, how might this affect knowledge dynamics already characterized by significant inequities?

The AI divide extends beyond simple access to include quality differentials in available systems. Even as simple AI tools become widely available, sophisticated capabilities that genuinely enhance knowledge work may remain concentrated within well-resourced institutions. This could lead to a scenario where practitioners in resource-constrained settings use rudimentary AI tools that produce low-quality outputs, further reinforcing perceptions of capability gaps between North and South.

Confronting power dynamics in AI integration

Traditional knowledge systems in global health position expertise in academic and institutional centers, with information flowing outward to practitioners who implement standardized solutions. This existing structure reflects and reinforces global power imbalances. 

The integration of AI within these systems could either exacerbate these inequities—by further concentrating knowledge production capabilities within well-resourced institutions—or potentially disrupt them by enabling more distributed knowledge creation processes.

Joseph’s journey demonstrates this tension. His adoption of AI tools might be viewed as an attempt to access capabilities otherwise reserved for those with greater institutional resources. The question becomes not whether to allow such adoption, but how to ensure it serves genuine knowledge democratization rather than simply producing more sophisticated simulations of participation.

These emerging dynamics require us to fundamentally rethink how knowledge is valued, created, and shared within global health networks. The transparency paradox, economic pressures, and emerging AI divide suggest that technological integration will not occur within neutral space but rather within contexts already characterized by significant power asymmetries.

Developing effective responses requires moving beyond simple prescriptions about AI adoption toward deeper analysis of how these technologies interact with existing power structures—and how they might be intentionally directed toward either reinforcing or transforming these structures.

My framework for Artificial Intelligence as co-worker to support networked learning and local action is intended to contribute to such efforts.

Illustration: The Geneva Learning Foundation Collection © 2025

References

Frehywot, S., Vovides, Y., 2024. Contextualizing algorithmic literacy framework for global health workforce education. AIH 0, 4903. https://doi.org/10.36922/aih.4903

Hazarika, I., 2020. Artificial intelligence: opportunities and implications for the health workforce. International Health 12, 241–245. https://doi.org/10.1093/inthealth/ihaa007

John, A., Newton-Lewis, T., Srinivasan, S., 2019. Means, Motives and Opportunity: determinants of community health worker performance. BMJ Glob Health 4, e001790. https://doi.org/10.1136/bmjgh-2019-001790

Newton-Lewis, T., Munar, W., Chanturidze, T., 2021. Performance management in complex adaptive systems: a conceptual framework for health systems. BMJ Glob Health 6, e005582. https://doi.org/10.1136/bmjgh-2021-005582

Newton-Lewis, T., Nanda, P., 2021. Problematic problem diagnostics: why digital health interventions for community health workers do not always achieve their desired impact. BMJ Glob Health 6, e005942. https://doi.org/10.1136/bmjgh-2021-005942

Artificial Intelligence and the health workforce: Perspectives from medical associations on AI in health (OECD Artificial Intelligence Papers No. 28), 2024. , OECD Artificial Intelligence Papers. https://doi.org/10.1787/9a31d8af-en

Sadki, R. (2025). A global health framework for Artificial Intelligence as co-worker to support networked learning and local action. Reda Sadki. https://doi.org/10.59350/gr56c-cdd51

Peer learning through Psychological First Aid: New ways to strengthen support for Ukrainian children

Peer learning for Psychological First Aid: New ways to strengthen support for Ukrainian children

Writing

This article is based on Reda Sadki’s presentation at the ChildHub “Webinar on Psychological First Aid for Children; Supporting the Most Vulnerable” on 6 March 2025. Learn more about the Certificate peer learning programme on Psychological First Aid (PFA) in support of children affected by the humanitarian crisis in Ukraine. Get insights from professionals who support Ukrainian children.

“I understood that if we want to cry, we can cry,” reflected a practitioner in the Certificate peer learning programme on Psychological First Aid (PFA) in support of children affected by the humanitarian crisis in Ukraine – illustrating the kind of personal transformation that complements technical training.

During the ChildHub “Webinar on Psychological First Aid for Children; Supporting the Most Vulnerable”, the Geneva Learning Foundation’s Reda Sadki explained how peer learning provides value that traditional training alone cannot deliver. The EU-funded program on Psychological First Aid (PFA) for children demonstrates that practitioners gain five specific benefits:

First, peer learning reveals contextual wisdom missing from standardized guidance. While technical training provides general principles, practitioners encounter varied situations requiring adaptation. When Serhii Federov helped a frightened girl during rocket strikes by focusing on her teddy bear, he discovered an approach not found in manuals: “This exercise helped the girl switch her focus from the situation around her to caring for the bear.”

Second, practitioners document pattern recognition across diverse cases. Sadki shared how analysis of practitioner experiences revealed that “PFA extends beyond emergency situations into everyday environments” and “children often invent their own therapeutic activities when given space.” These insights help practitioners recognize which approaches work in specific contexts.

Third, peer learning validates experiential knowledge. One practitioner described how simple acknowledgment of feelings often produced visible relief in children, while another found that basic physical comforts had significant psychological impact. These observations, when shared and confirmed across multiple practitioners, build confidence in approaches that might otherwise seem too simple.

Fourth, the network provides real-time problem-solving for urgent challenges. During fortnightly PFA Connect sessions, practitioners discuss immediate issues like “supporting children under three years” or “recognizing severe reactions requiring referrals.” As Sadki explained, these sessions produce concise “key learning points” summarizing practical solutions practitioners can immediately apply.

Finally, peer learning builds professional identity and resilience. “There’s a lot of trust in our network,” Sadki quoted from a participant, demonstrating how sharing experiences reduces isolation and builds a supportive community where practitioners can acknowledge their own emotions and challenges.

The webinar highlighted how this approach creates measurable impact, with practitioners developing case studies that transform tacit knowledge into documented evidence and structured feedback that helps discover blind spots in their practice.

For practitioners interested in joining, Sadki outlined multiple entry points from microlearning modules completed in under an hour to more intensive peer learning exercises, all designed to strengthen support to children while building practitioners’ own professional capabilities.

This project is funded by the European Union. Its contents are the sole responsibility of TGLF, and do not necessarily reflect the views of the European Union.

Illustration: The Geneva Learning Foundation Collection © 2025

New ways to learn and lead HPV vaccination

AI podcast explores surprising insights from health workers about HPV vaccination

Global health

This is an AI podcast featuring two hosts discussing an article by Reda Sadki titled “New Ways to Learn and Lead HPV Vaccination: Bridging Planning and Implementation Gaps.” The conversational format involves the AI hosts taking turns explaining key points and sharing insights about Sadki’s work on HPV vaccination strategies. While the conversation is AI-generated, everything is based on the published article and insights from the experiences of thousands of health workers participating in Teach to Reach.

The Geneva Learning Foundation’s approach

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:

  1. Gathering experiences from health workers worldwide
  2. Analyzing these experiences for patterns and innovative solutions
  3. Conducting deep dives into specific case studies
  4. Bringing national EPI planners into the conversation
  5. 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.

New ways to learn and lead HPV vaccination Bridging planning and implementation gaps

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

Global health

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

The HPV vaccination implementation challenge

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

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

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

How the peer learning network alternative can support HPV vaccination

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

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

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

HPV vaccination: the peer learning cycle in action

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

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

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

Teach to Reach: Building a learning community for HPV vaccination

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

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

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

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

Through this process, we uncovered several important implementation insights:

1. Importance of connecting field experience to policy

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

2. Implementation learning

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

3. Unexpected implementation findings

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

4. Counter-intuitive success factors

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

5. Unexpected challenges

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

6. Examples of novel solutions

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

System-level surprises

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

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

The power of health worker collective intelligence

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

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

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

Continuous learning: The key to improvement

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

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

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

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

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

Impact and benefits of peer learning

This approach delivers multiple benefits:

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

Key success factors include:

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

How can we interpret these findings?

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

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

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

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

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

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

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

Accelerating HPV vaccination progress

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

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

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

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

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

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

Acknowledgements

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

References

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

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

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

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

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

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

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

Umbelino-Walker, I. et al. (2024) ‘Towards a sustainable model for a digital learning network in support of the Immunization Agenda 2030 –a mixed methods study with a transdisciplinary component’, PLOS Global Public Health. Edited by M. Pentecost, 4(12), p. e0003855. Available at: https://doi.org/10.1371/journal.pgph.0003855.

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

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

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

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

Global health

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

The promise of connected communities

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

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

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

Understanding Artificial Intelligence (AI) as partner, not tool

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

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

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

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

A framework for AI partnership in global health

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

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

Applications across three global health challenges

This framework suggests new possibilities for addressing persistent challenges.

1. Immunization systems

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

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

2. Neglected Tropical Diseases (NTDs)

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

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

3. Climate change and health

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

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

Pandemic preparedness beyond early warning

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

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

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

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

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

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

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

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

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

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

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

Investment for innovation

Realizing this vision requires strategic investment in:

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

Looking forward

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

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

7 key principles for AI partnership

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

Listen to an AI-generated podcast about this article

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

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

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

Image: The Geneva Learning Foundation Collection © 2024

Supporting Ukrainian children: New peer learning platform to rapidly expand and scale the network of practitioners across Europe

Supporting Ukrainian children: New peer learning platform to rapidly expand and scale the network of practitioners across Europe

Global health

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”.

See what we learned from 873 practitioners…

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.

Peer learning in immunisation programmes

Peer learning in immunization programmes

and Global health

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.

Learn more: 50 years of the Expanded Programme on Immunization (EPI)

Beyond traditional knowledge exchange

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.

Learn more: Movement for Immunization Agenda 2030 (IA2030): National EPI leaders from 31 countries share experience of HPV vaccination

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:

  1. Granular understanding of implementation challenges while maintaining systematic rigor in knowledge capture;
  2. Documentation of practical innovations while creating frameworks for adaptation across contexts; and
  3. 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

Image: The Geneva Learning Foundation Collection © 2024

An AI-generated podcast dialogue exploring The Geneva Learning Foundation’s progress in 2024

A generative AI podcast dialogue exploring The Geneva Learning Foundation’s progress in 2024

Global health

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.

    The cost of inaction Quantifying the impact of climate change on health

    The cost of inaction: Quantifying the impact of climate change on health

    Global health

    This World Bank report ‘The Cost of Inaction: Quantifying the Impact of Climate Change on Health in Low- and Middle-Income Countries’ presents new analysis of climate change impacts on health systems and outcomes in the regions that are bearing the brunt of these impacts.

    Key analytical insights to quantify climate change impacts on health

    The report makes three contributions to our understanding of climate-health interactions:

    First, it quantifies the massive scale of climate change impacts on health, projecting 4.1-5.2 billion climate-related disease cases and 14.5-15.6 million deaths in LMICs by 2050. This represents a significant advancement over previous estimates, which the report demonstrates were substantial underestimates.

    Second, it illuminates the profound economic consequences, calculating costs of $8.6-20.8 trillion by 2050 (0.7-1.3% of LMIC GDP). The report employs both Value of Statistical Life and Years of Life Lost approaches to provide a range of economic impact estimates.

    Third, it reveals stark geographic inequities in impact distribution, with Sub-Saharan Africa bearing approximately 71% of cases and nearly half of deaths, while South Asia faces about 18% of cases and a quarter of deaths. This spatial analysis helps identify where interventions are most urgently needed.

    Policy implications and systemic perspectives

    The report’s findings point to several critical policy directions:

    • The need for systemic rather than disease-specific interventions emerges as a central theme. The authors explicitly advocate for strengthening entire health systems rather than pursuing vertical disease programs.
    • The economic analysis makes a compelling case for immediate action, demonstrating that the costs of inaction far exceed potential investment requirements for climate-resilient health systems.
    • The geographic distribution of impacts highlights the need for globally coordinated responses while prioritizing support for the most vulnerable regions.

    The findings suggest that transforming systems to address climate change impacts on health requires not just technical solutions but fundamental rethinking of how health systems are organized and financed in vulnerable regions.

    This aligns with recent scholarship on complex adaptive systems and organizational transformation in global health.

    The report’s emphasis on systemic approaches represents a significant shift in thinking about climate-health interventions. This merits unpacking on several levels:

    1. 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.
    2. 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.
    3. Implementation implications: The report suggests this systemic approach requires: integrated planning across health system components, flexible funding mechanisms that support system-wide capabilities, enhanced coordination between different health programmes and investment in cross-cutting infrastructure and capabilities.

    What about the health workforce facing impacts of climate change on health?

    Between this clear-eyed assessment and effective action lies a critical implementation gap.

    Interestingly, the report gives limited explicit attention to the health workforce dimension of climate-health challenges. Yet that is precisely where we need to focus attention, given that:

    • Health workers based in communities are first responders to climate-related health emergencies
    • Workforce capacity significantly determines a health system’s adaptive capabilities
    • Climate change itself affects health worker distribution and effectiveness

    Given the report’s emphasis on systemic approaches, the lack of detailed discussion about human resources for health represents a missed opportunity to explore what effective action might look like.

    The Geneva Learning Foundation’s network, developed through nearly a decade of research and practice, has led us to identify a path for supporting the health workforce to strengthen preparedness and response in response to climate change impacts on health.

    The network already connects over 60,000 health workers. They represent all job roles, rank, and levels of the health system.

    One distinguishing feature of this network is its deep integration with existing government health systems. Over half of network participants are government employees, from community health workers to district officers to national planners.

    62% of participants work in remote rural areas, 47% serve urban poor populations, and 21% operate in conflict zones.

    These are not just statistics: they represent an unprecedented capability to mobilize knowledge and action where it’s most needed.

    Since 2023, network participants have been sharing observations, experiences, and insights of climate change impacts on health. 

    The model connects different levels of health systems:

    • Community-based health workers share ground-level observations
    • District managers identify emerging patterns
    • National planners gauge system-wide implications
    • Global partners access real-time insights

    When a malaria control officer in Kenya observes changing disease patterns due to altered rainfall, the network enables rapid sharing of this insight with colleagues working on water safety, nutrition, and primary care. These cross-domain connections do not need to be left to chance – they can be enabled through structured peer learning processes that transcend traditional programme, geographic, and hierarchical boundaries

    This creates what organizational theorists call “embedded transformation” – where system change emerges through existing structures rather than requiring new ones.

    Rather than creating new coordination mechanisms, the network enables:

    • Health workers to learn directly from peers in other programs
    • Rapid identification of cross-cutting challenges
    • Spontaneous formation of problem-solving groups
    • Systematic sharing of effective practices

    Rather than replacing existing structures, TGLF’s model demonstrates how digital networks can enable health systems to:

    • Maintain necessary specialization while fostering crucial connections
    • Enable rapid learning and adaptation across programs
    • Optimize resource use through enhanced coordination
    • Build system-wide resilience through structured peer learning

    Such a network enables what complexity theorists call “distributed sensing” that can provide:

    • Early warning of emerging threats
    • Rapid sharing of local solutions
    • System-wide learning from local innovations
    • Continuous adaptation to changing conditions

    This has led us to posit that investment in such emergent digital networks could enable health systems to maintain necessary specialization while fostering crucial connections across domains.

    This is obviously critical to respond to the systems-level complexity of climate change impacts on health.

    World Bank findingTGLF model strategic 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 interventionThe network transcends traditional program boundaries through what organizational theorists call “structured emergence” – practitioners naturally form cross-program connections based on shared challenges. When a malaria control officer observes changing disease patterns due to climate shifts, the network enables rapid sharing with colleagues in water safety, nutrition, and primary care. This organic integration emerges through peer learning rather than requiring new coordination mechanisms.
    Urgency of investmentTGLF’s model offers an immediately scalable approach that builds on existing health system capabilities. Rather than waiting years to develop new infrastructure, the network can rapidly expand to connect more practitioners and regions. Evidence shows 7x acceleration in implementation of new approaches compared to conventional means of technical assistance, suggesting potential for rapid, sustainable strengthening of health system resilience.
    Global coordination needWhile enabling global connection, the network maintains strong local grounding through its emphasis on locally-led action and contextual adaptation. Government health workers comprise over 50% of participants, creating what scholars term “embedded transformation” – change emerging through existing structures rather than imposed from outside. This enables coordinated response while respecting local health system authority.
    System transformationThe model demonstrates how digital networks can fundamentally transform how health systems operate without requiring complete restructuring. By enabling rapid knowledge flow across traditional boundaries, supporting emergence of new coordination patterns, and fostering system-wide learning, it shows how transformation can emerge through enhanced connection rather than structural overhaul. Analysis reveals development of new capabilities in surveillance, response, and adaptation through networked learning.

    Reference

    Uribe, J.P., Rabie, T., 2024. The Cost of Inaction: Quantifying the Impact of Climate Change on Health in Low- and Middle-Income Countries. The World Bank, Washington, D.C. https://doi.org/10.1596/42419

    Image: The Geneva Learning Foundation Collection © 2024