Comparative analysis of workforce development models in the global malaria elimination agenda

The stagnation in global malaria mortality reduction has forced a re-evaluation of the tools and strategies currently deployed in high-burden countries.

While biological challenges such as insecticide resistance and parasite mutations are well-documented, a critical bottleneck remains the capacity of the human workforce to implement technical strategies with precision.

The transition from control to elimination requires a fundamental shift in workforce development.

It demands moving beyond the passive transmission of technical knowledge toward models that recognize the value of the health worker.

People who work for health, especially those who engage directly with communities, are likely to possess unique insights into local transmission dynamics and community behavior.

This analysis reviews four predominant capacity-building architectures currently active in the malaria landscape.

These initiatives are assessed based on their ability to scale to the district and community levels, their cost-effectiveness, and their capacity to validate and utilize the tacit knowledge held by local staff.

Malaria learning model 1. The academic massive open online course model

The most prominent example of the digital transmission model is the MalariaX series offered by Harvard University.

This initiative utilizes the Massive Open Online Course (MOOC) format to democratize access to high-level scientific knowledge.

Strengths

The primary strength of this model is the unparalleled quality of its technical content.

It provides participants in low-resource settings with direct access to global experts and the latest scientific evidence regarding vector biology, epidemiology, and immunology.

The digital format allows for infinite scalability in terms of access.

Anyone with an internet connection can technically access the material.

This eliminates the geographical barriers that often exclude peripheral health workers from elite training.

Limitations

The model suffers from the “know-do” gap.

While it effectively transmits theoretical knowledge, it lacks a structural mechanism to ensure this knowledge is applied to local realities.

The pedagogy relies heavily on passive consumption of video lectures which reinforces the hierarchy of “expert” versus “learner.”

It fails to account for the specific needs of local health workers who must adapt global scientific principles to context-specific challenges, such as unexpected climate shifts or community resistance.

The assessment mechanisms verify knowledge retention rather than the ability to navigate these local complexities.

Consequently, it undervalues the learner’s own experience and offers no channel for the “global expert” to learn from the “local expert” who is managing the disease daily.

Malaria learning model 2. The normative cascade training model

The World Health Organization (WHO) and national malaria programs typically rely on the cascade model to disseminate new guidelines.

This approach involves training a core group of master trainers at the national level who then train regional officers, who in turn train district and facility staff.

Strengths

This model ensures strong alignment with national policy and global normative guidance.

It maintains a clear chain of command and reinforces the authority of the Ministry of Health.

It is particularly effective for standardization, such as ensuring that a specific treatment protocol for severe malaria is introduced uniformly across the health system.

Weaknesses

The cascade model is plagued by the dilution of quality as training moves down the chain.

Information is frequently distorted or simplified by the time it reaches the community health worker.

Structurally, it treats the health worker as a passive vessel to be filled with instructions rather than a thinking professional who understands the local ecosystem.

It is also prohibitively expensive and logistically heavy.

It often relies on per diems that distort participant motivation and create a “training aristocracy” where access is determined by seniority rather than need.

Crucially, this model often interprets local adaptation as non-compliance.

It fails to recognize that frontline workers often deviate from protocols not out of ignorance but out of necessity, driven by supply chain ruptures or specific community demands that only they understand.

Malaria learning model 3. The fellowship model

Initiatives such as the African Leadership and Management Training for Impact in Malaria Eradication (ALAMIME) represent the fellowship model.

These programs target high-potential program managers for intensive, long-term leadership development, often in partnership with universities.

Strengths

This model addresses the critical “soft skills” gap identified in malaria elimination policy reviews.

It moves beyond technical biology to teach management, advocacy, and financial planning.

By focusing on African leadership, it actively works to decolonize the expertise hierarchy and fosters strong regional ownership.

The cohort-based approach builds deep professional bonds among future leaders of national malaria programs.

Weaknesses

The fundamental limitation is scalability and exclusivity.

These programs are resource-intensive and reach a small number of individuals per year.

While they produce high-quality leaders at the top, they cannot reach the critical mass of district and community personnel required to execute malaria strategies.

This reinforces a top-heavy leadership structure that ignores the need for “micro-leadership” at the facility level.

It overlooks the reality that a district nurse or community health worker must also exercise leadership and diplomacy every day to secure community trust.

By focusing on the elite, this model inadvertently devalues the significance of the leadership required at the last mile.

Malaria learning model 4. The field epidemiology training program model

The Field Epidemiology Training Program (FETP) functions as a learning-by-doing apprenticeship.

Residents work within the health system to investigate outbreaks and analyze surveillance data under the mentorship of experienced epidemiologists.

Strengths

This model closely aligns learning with work.

It is an “applied” model where the output of the training is often a tangible public health product.

It effectively builds data literacy and analytical capacity.

It grounds the learner in the reality of the field rather than the theory of the classroom.

Weaknesses

Like the fellowship model, the FETP is difficult to scale due to the requirement for intense, one-on-one mentorship.

It is a high-cost intervention per learner.

Furthermore, the rigorous focus on surveillance and epidemiology often overshadows the operational implementation challenges faced by generalist health workers.

While it produces excellent surveillance officers, it does not necessarily equip the broader workforce to utilize their own data for local decision-making.

It often extracts data for central analysis rather than empowering local staff to interpret the trends they witness daily.

This failure to devolve analytical power ignores the fact that local workers are often the first to notice anomalies, such as climate-driven shifts in vector behavior, long before they appear in national databases.

Four recommendations to strengthen malaria learning and capacity-building

The current landscape of malaria capacity building reveals a functional and epistemic schism.

The academic and normative models excel at defining what needs to be done but fail to support the workforce in how to do it within their specific constraints.

The fellowship and apprenticeship models build deep capacity but are structurally incapable of reaching the volume of workers necessary for elimination.

A significant gap exists for a model that combines the scalability of digital platforms with the implementation rigor of the apprenticeship approach.

To achieve malaria elimination, future initiatives need to:

References

General context & the “know-do” gap

• Mwenesi, H., Mbogo, C., Casamitjana, N., Castro, M. C., Itoe, M. A., Okonofua, F., & Tanner, M. (2022). Rethinking human resources and capacity building needs for malaria control and elimination in Africa. PLOS Global Public Health, 2(5), e0000210.
  https://doi.org/10.1371/journal.pgph.0000210
• World Health Organization. (2016). Global Technical Strategy for Malaria 2016–2030. Geneva: World Health Organization.
  https://www.who.int/docs/default-source/documents/global-technical-strategy-for-malaria-2016-2030.pdf

Model 1: The academic MOOC model (MalariaX)

• Harvard University. MalariaX: Defeating Malaria from the Genes to the Globe. Harvard Online.
  https://www.harvardonline.harvard.edu/course/malariax-defeating-malaria-genes-globe
• Wirth, D. F., Casamitjana, N., Tanner, M., & Reich, M. R. (2018). Global action for training in malaria elimination. Malaria Journal, 17(1), 51.
  https://doi.org/10.1186/s12936-018-2199-3

Model 2: The normative cascade model and incentives

• Dambisya, Y. M., & Matinhure, S. (2012). Policy Brief: Perceptions of per diems in the health sector: Evidence and implications. U4 Anti-Corruption Resource Centre.
  https://www.cmi.no/publications/file/4082-perceptions-of-per-diems-in-the-health-sector.pdf
• Maes, K., 2012. Volunteerism or Labor Exploitation? Harnessing the Volunteer Spirit to Sustain AIDS Treatment Programs in Urban Ethiopia. Human Organization 71, 54–64. https://doi.org/10.17730/humo.71.1.axm39467485m22w4

Model 3: The fellowship model (ALAMIME)

• ALAMIME. African Leadership and Management Training for Impact in Malaria Eradication. Makerere University School of Public Health.
  https://alamime.musph.ac.ug/
• Couper, I., Ray, S., Blaauw, D., et al. (2018). Curriculum and training needs of mid-level health workers in Africa: a situational review from Kenya, Nigeria, South Africa and Uganda. BMC Health Services Research, 18(1), 553.
  https://doi.org/10.1186/s12913-018-3362-9

Model 4: The Field Epidemiology Training Program (FETP)

• Centers for Disease Control and Prevention (CDC). Education and Training | Parasites.
  https://www.cdc.gov/parasites/education_training/education-training.html
• Neta, G., Brownson, R. C., & Chambers, D. A. (2018). Opportunities for Epidemiologists in Implementation Science: A Primer. American Journal of Epidemiology, 187(5), 899–910.
  https://doi.org/10.1093/aje/kwx323

Strategic recommendations and value validation

• The Geneva Learning Foundation. (2024). Teach to Reach 10: Over 21,000 Health Workers Unite To Tackle Climate and Immunization Challenges. Health Policy Watch.
  https://healthpolicy-watch.news/teach-to-reach-10-over-21000-health-workers-unite-to-tackle-climate-and-immunization-challenges/
• Sadki, R. (2025). When funding shrinks, impact must grow: the economic case for peer learning networks.
  https://doi.org/10.59350/redasadki.20995

Rethinking human resources for malaria control and elimination in Africa

The comprehensive policy review by Halima Mwenesi and colleagues “Rethinking human resources and capacity building needs for malaria control and elimination in Africa” argues that the stagnation in global malaria progress is fundamentally a human resources crisis rather than solely a biological or technical failure.

The authors posit that the current workforce is insufficient in number and ill-equipped with the necessary skills to navigate the complex transition from malaria control to elimination.

It is a critical indictment of the status quo in malaria training and offers a roadmap for structural reform.

This article summarizes key points from the policy review and examines how The Geneva Learning Foundation’s peer learning-to-action model could be used by national programmes to transform the health workforce.

The mismatch between training and operational needs

The authors identify a severe imbalance in training priorities where capacity building has historically favored biomedical and basic sciences such as entomology and parasitology.

While essential, this focus has led to a neglect of operational, translational, and implementation sciences.

The report highlights that while the global community produces high-level scientists who understand the parasite, it fails to produce “translational scientists” who can bridge the gap between global guidelines and local realities.

This has resulted, they argue, in a workforce lacking the practical competencies to operationalize complex elimination strategies that require precision and adaptation.

The deficit in leadership and social sciences

A major finding is the specific deficit in so-called “soft skills” and social sciences which are increasingly critical as programs move toward elimination.

The authors argue that modern malaria control requires competencies in leadership, health diplomacy, anthropology, sociology, and political analysis.

Program managers currently lack the training to navigate complex political landscapes, mobilize domestic resources, or engage effectively with communities to sustain interventions.

The review emphasizes that understanding community behavior and social determinants is as critical as understanding vector behavior but this is rarely reflected in curricula.

Data illiteracy and the failure of surveillance

The paper identifies pervasive “data illiteracy” across the workforce.

Health workers collect vast amounts of data to satisfy donor reporting requirements but often lack the skills to interpret or use it for local decision-making.

This results in a “data-rich but information-poor” environment.

As countries move toward elimination, the need for real-time, granular surveillance becomes paramount.

The current workforce is unable to perform the rapid data analysis required to detect and respond to outbreaks at the sub-national level.

Fragmentation and lack of coordination

The review critiques the fragmentation of investments in training, capacity-building, and technical assistance driven by donor agendas.

It notes a lack of coordination among donors and agencies which leads to a proliferation of uncoordinated short courses and workshops that do not necessarily align with national strategic plans.

This fragmentation is exacerbated by a lack of data on the workforce itself.

Many countries lack a central registry of malaria personnel which makes it impossible to forecast needs, plan for attrition, or manage career pathways.

The call for structural transformation

The authors call for a radical shift toward “South-South” collaboration where African institutions take the lead in training.

They advocate for moving away from ad hoc workshops toward institutionalized, long-term capacity building.

Crucially, they recommend the use of digital platforms to democratize access to knowledge for mid-level and community-based cadres who are often excluded from elite fellowships.

How can learning science help transform malaria training investments into tangible health worker performance?

For a global health epidemiologist accustomed to viewing disease control through the lens of biological interventions and coverage rates, the human resource crisis described by Mwenesi and colleagues represents a “delivery failure” of validated tools.

The Geneva Learning Foundation (TGLF) learning science model functions as a structural intervention designed to repair broken delivery mechanisms in global health and humanitarian response.

The following analysis translates the TGLF approach into terms recognizable to an epidemiologist or program manager who operates with the assumption that training is primarily about the transmission of technical knowledge.

Moving from passive transmission to implementation fidelity

Epidemiologists understand that a vaccine with high efficacy in a trial often has low effectiveness in the real world due to poor administration or cold chain failure.

Similarly, Mwenesi et al. identify that technical malaria guidelines fail because the “human infrastructure” cannot implement them.

Traditional training assumes that if you lecture health workers on a protocol, which is a transmission of information, they will execute it.

This is a “single-loop” assumption.

The TGLF model introduces an “implementation loop.”

Instead of merely receiving information, learners in the TGLF network must design a micro-project to apply the new guideline in their specific district, execute it, and report back on the results using their own local data.

This turns the workforce from passive recipients of protocols into active testers of implementation fidelity.

It directly addresses the “translational science” gap identified in the paper by forcing the learner to translate theory into practice immediately.

Sceptics often argue that this approach places an undue burden on an already overworked workforce.

However, the TGLF model embeds learning into the workflow itself.

This is not additional work but rather “learning-based work.”

Participants do not create hypothetical projects.

They identify a bottleneck they are currently facing, such as a specific pocket of malaria transmission, and use the learning cycle to address it.

This transforms the training from an external interruption into an operational support mechanism.

By embedding learning into the workflow, it operationalizes Mwenesi’s call for translational science.

It considers the daily struggle of the health worker as a form of structured scientific inquiry: they hypothesize a solution, test it, and report the results.

This is implementation as science.

Operationalizing data use for local decision-making

Mwenesi notes that health workers collect data but do not use it.

In the TGLF model, data is not something sent “up” to the ministry.

It is the raw material for peer support and feedback.

In a TGLF peer learning exercise, a district medical officer in Ghana shares their case management data to compare performance with a peer in Uganda.

They share because they want to, not because they are required to.

This creates a social incentive to understand and analyze one’s own data.

It builds the “data literacy” the authors call for not through abstract statistics courses but through the practical necessity of explaining one’s own performance to a colleague.

This process transforms data from a compliance burden into a tool for local problem-solving.

Is there a risk that peer learning will pool ignorance?

Is there a valid concern regarding the risk of “pooled ignorance” where peers might reinforce incorrect practices?

The TGLF model mitigates this through “structured emergence.”

The model does not dismiss expert knowledge but uses global guidelines as the “anchor” for local problem-solving.

In this system, a health worker cannot simply state an opinion.

They must submit an action plan that is peer-reviewed against a rubric derived from WHO guidelines.

This process ensures fidelity to technical standards while allowing for necessary local adaptation.

The aggregation of thousands of these peer-reviewed plans creates a new form of rigorous, practice-based evidence that complements expert guidance.

Scaling “soft skills” through structured peer review

The review calls for leadership and diplomacy skills but notes these are hard to teach in workshops.

The TGLF model builds these skills implicitly through its pedagogical structure.

When a participant submits an action plan, they must receive and respond to critical feedback from peers in other countries.

They must negotiate differing viewpoints and defend their technical choices.

This mimics the “health diplomacy” and leadership dynamics required in real-world program management.

Furthermore, because they must engage community stakeholders to implement their projects, they practice the anthropological and social engagement skills Mwenesi identifies as missing.

They learn leadership not by studying a theory of leadership but by leading a change initiative in their facility.

While some experts argue that soft skills require “hard contact” in physical spaces, TGLF results suggest that physical proximity often limits a worker to their known environment and existing biases.

The TGLF model introduces a form of “cosmopolitan localism.”

When a nurse in rural Nigeria must explain her challenge to a peer in urban India, she is forced to articulate her context with a clarity and diplomacy not required when speaking to a neighbor.

This defiance of distance fosters a quantum leap in communication capabilities.

Participants report that the skills learned in negotiating these digital, cross-cultural peer relationships directly translate to better engagement with their physical-world colleagues and community leaders.

Addressing the incentive structure and correcting expertise asymmetry

The paper critiques the “brain drain” and the reliance on experts from the Global North.

TGLF operationalizes the “South-South” collaboration recommended by the authors by creating a flat digital hierarchy.

In this model, the “expert” is not a visiting consultant from Geneva but a peer who has successfully solved the problem in their own context.

A nurse in Nigeria learns how to improve bed net usage from a nurse in Kenya who solved that exact refusal issue last month.

This actually results in greater interest, comprehension, and use of official guidelines.

It also validates local knowledge and creates the “critical mass of thinking professionals” that Mwenesi argues is essential for elimination.

It shifts the source of authority from external experts to the collective intelligence of the network.

Transforming the economy of per diem

A common critique of moving away from face-to-face training is the reliance of health workers on per diems for financial survival.

Mwenesi implies that the current system is unsustainable.

The TGLF model operates on the evidence that per diem-driven training often restricts access to a “training aristocracy” of recurrent participants while excluding the frontline workers who most need the knowledge.

TGLF replaces the financial incentive with a professional survival incentive.

In the Nigeria Immunization Collaborative, over 4,300 health workers participated without per diems.

They did so because the program addressed the specific pain points of their daily work.

This filters the workforce for “positive deviants,” or those with high intrinsic motivation who are most likely to drive elimination efforts, rather than those primarily motivated by daily subsistence allowances.

A “surveillance system” for human resources and performance

Finally, the review notes the lack of registries and data on the workforce itself.

The TGLF digital network acts as a real-time sensor of workforce capacity.

By engaging thousands of health workers simultaneously, the platform generates data on who is active, what problems they are facing, and where their skills are deficient.

For an epidemiologist, this is equivalent to a surveillance system for human resources.

It provides the visibility needed to forecast gaps and target interventions precisely, replacing the “blind” proliferation of uncoordinated workshops with a data-driven approach to capacity building.

Regarding concerns that digital platforms fail in low-resource settings due to poor connectivity, TGLF utilizes a “cognitively quiet” design that functions on low-bandwidth connections and mobile devices.

This design respects the technological reality of the African context.

Data from the Teach to Reach program, which has engaged over 60,000 participants in remote, ongoing peer learning activities , demonstrates that when the technology is adapted to the user rather than the other way around, participation rates exceed those of physical workshops.

This scale allows for the identification of systemic patterns and workforce gaps that would be invisible in a smaller, face-to-face cohort.

Reference

Mwenesi, H., Mbogo, C., Casamitjana, N., Castro, M.C., Itoe, M.A., Okonofua, F., Tanner, M., 2022. Rethinking human resources and capacity building needs for malaria control and elimination in Africa. PLOS Glob Public Health 2, e0000210. https://doi.org/10.1371/journal.pgph.0000210

Reda Sadki (2023). How do we reframe health performance management within complex adaptive systems?. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/mx5qr-qet97

Reda Sadki (2024). Prioritizing the health and care workforce shortage: protect, invest, together. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/zzqr4-9g482

Reda Sadki (2024). Protect, invest, together: strengthening health workforce through new learning models. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/g24b4-7fj64

Reda Sadki (2024). What is double-loop learning in global health?. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/s4xtw-b7274

Reda Sadki (2024). World Malaria Day 2024: We need new ways to support health workers leading change with local communities. Reda Sadki: Learning to make a difference. https://doi.org/10.59350/yrn1r-hpz62

5 surprising insights from the science of successful learning

The work of Reda Sadki offers a provocative, often counter-intuitive critique of how we learn, lead, and solve complex problems.

Here are five surprising insights from his body of work.

1. Text is superior to video for learning

In an era where educational technology is obsessed with video content, immersive simulations, and flashy multimedia, Sadki argues for the humble written word.

He asserts that the push for multimedia is often a “deception” that confuses engagement with entertainment.

In Richard Mayer’s research on multimedia for learning actually proves text works better, Sadki re-examines the foundational science of instructional design.

He points out that multimedia often creates “cognitive waste” by forcing the brain to split attention between visual and auditory streams.

He argues that well-structured text is “cognitively quiet” and far better suited for the high-level critical thinking required in complex fields.

He doubles down on this in Against chocolate-covered broccoli: text-based alternatives to expensive multimedia content.

Here, he describes multimedia as an economic dead end.

He argues that text is not only cheaper and easier to update but also creates a more equitable learning environment for professionals in low-bandwidth settings.

2. Gamification is a “disaster” for humanitarian learning

While many organizations rush to “gamify” learning with badges, points, and leaderboards, Sadki calls this trend a “dead end.”

He argues that gamification is simply “lipstick on the pig of behaviorism,” a discredited theory that treats learners like rats in a maze responding to stimuli.

In Why gamification is a disaster for humanitarian learning, he makes a blistering case that games fail to model the complexity of the real world.

He points out that the dominant culture of video games often relies on violence and competition, which are antithetical to humanitarian values.

He argues that professionals facing life-and-death decisions need critical reasoning skills, not the artificial dopamine hits of a game.

3. Low completion rates can be a sign of success, not failure

In the world of online courses, a low completion rate is usually seen as a failure of design.

Sadki flips this metric on its head.

He suggests that in professional settings, “completion” is a vanity metric, part of the legacy of education systems that kept learners in closed environments.

In Online learning completion rates in context: Rethinking success in digital learning networks, he argues that busy professionals often engage with learning to solve a specific problem.

Once they find the solution, they leave.

This “drop-off” is actually efficient learning in action.

He warns that optimizing for completion often leads to dumbing down content rather than increasing its impact.

4. The “transparency paradox”: health workers are using AI in secret

One of Sadki’s most startling recent observations comes from his work with frontline health workers.

He reveals that professionals in the Global South are already using advanced Artificial Intelligence (AI) tools, but they are forced to hide this fact.

In Artificial intelligence, accountability, and authenticity: knowledge production and power in global health crisis, he describes a “transparency paradox.”

Global health systems are often punitive.

If a health worker admits to using AI to help draft a report or analyze data, their work is devalued as “inauthentic,” even if the quality is higher.

This forces innovation underground and prevents organizations from learning how to effectively partner with AI.

He expands on the solution in A global health framework for Artificial Intelligence as co-worker to support networked learning and local action, arguing that we must legitimize AI as a “co-worker” rather than a cheat.

5. Cascade training is mathematically doomed to fail

Finally, Sadki uses simple mathematics to dismantle one of the most common methods of training in the world: the “cascade” model, where experts train trainers, who train others.

In Why does cascade training fail?, he demonstrates that information loss at every level of the cascade is inevitable.

He argues that this model persists not because it works, but because it is convenient for hierarchical organizations.

He offers a stark alternative in Calculating the relative effectiveness of expert coaching, peer learning, and cascade training, where he proves that peer learning networks are the only model capable of scaling without losing quality.

5 reasons why our current systems of learning are broken – and how to fix them

Reda Sadki’s writing explores how systems of learning matter when tackling complex challenges across global health, humanitarian aid, and education.

Over twelve years of articles on his blog, he has built a cohesive argument for why our current systems of learning are broken and how we might fix them.

Since 2016, his work at The Geneva Learning Foundation has demonstrated how to turn such rethinking into new ways to learn and lead in the face of critical threats to our societies.

Here are five themes that define his work.

1. The failure of traditional systems of learning and the peer learning alternative

One of Sadki’s most persistent arguments is that the humanitarian and global health sectors are addicted to ineffective models of training.

He questions the “workshop culture” that flies experts around the world at great cost with little measurable impact.

He argues that this “sage on the stage” model assumes knowledge flows only one way: from the expert to the ignorant practitioner.

He is equally critical of digital replacements that merely replicate this dynamic.

In Why gamification is a disaster for humanitarian learning, he warns that dressing up behaviorist drills with points and badges does not foster the critical thinking needed in crisis zones.

He expands on this in Experience and blended learning: two heads of the humanitarian training chimera, arguing that “transmissive” learning fails to prepare professionals for volatility and complexity.

Instead, Sadki advocates for peer learning networks where practitioners teach and learn from each other.

As he explains in What learning science underpins peer learning for Global Health?, the goal is not to transmit information but to foster the “co-creation” of new knowledge that is directly applicable to local contexts.

2. Epistemic justice: valuing communities as systems of learning

Sadki frequently uses the philosophy of Donald Schön to distinguish between the “high ground” of theory and the “swampy lowlands” of practice.

He argues that global health suffers from “epistemic injustice” – a systematic devaluation of the experiential knowledge held by local health workers.

In Knowing-in-action: Bridging the theory-practice divide in global health, he makes the case that the gap between global guidelines and local reality can only be bridged by recognizing frontline workers as knowledge creators, not just recipients.

He challenges the hierarchy that dismisses local insights as mere “anecdote.”

In Anecdote or lived experience: reimagining knowledge for climate-resilient health systems, he proposes a new framework where the collective stories of thousands of health workers shape a new, rigorous form of evidence.

In Critical evidence gaps in the Lancet Countdown on health and climate change, he points out that the most rigorous science can miss the vital signals that only those working in communities can see.

3. Artificial intelligence as a co-worker

While many in education view Artificial Intelligence (AI) as a threat to integrity or a tool for cheating, Sadki frames it as a transformative partner.

He argues that we are entering a new epoch where AI will not just be a tool we use, but a “co-worker” we collaborate with.

In A global health framework for Artificial Intelligence as co-worker to support networked learning and local action, he outlines how AI can support the “human” parts of learning – such as feedback and synthesis – without replacing human agency.

He explores the profound shifts in how we will interact with technology in The agentic AI revolution: what does it mean for workforce development?, describing a future where “AI agents” handle coordination, freeing humans to focus on judgment and ethics.

He pushes this further in Why YouTube is obsolete: From linear video content consumption to AI-mediated multimodal knowledge production, suggesting that AI will fundamentally change how we consume information, moving us away from linear formats like video lectures toward dynamic, interactive knowledge creation and retrieval.

4. Learning culture as the driver of learning systems

Sadki insists that learning is not an event but a culture.

Drawing heavily on the research of Karen E. Watkins and Victoria Marsick, he argues that an organization’s “learning culture” is the single best predictor of its ability to adapt and perform.

In Learning culture: the missing link in global health between learning and performance, he explains that without a culture that supports inquiry, dialogue, and risk-taking, even the best training programs will fail.

He identifies specific weaknesses in current systems, noting in Why lack of continuous learning is the Achilles heel of immunization that health systems often prioritize task completion over the continuous learning necessary to improve those tasks.

This theme connects deeply to leadership.

He argues in What is the relationship between leadership and performance? that true leadership is not about authority but about fostering an environment where learning can happen at every level of the hierarchy.

5. New ways to bridge the gap from policy to action

Finally, Sadki focuses relentlessly on the “know-do” gap, the disconnect between global policy and local implementation.

He argues that guidelines often fail because they are designed without the input of those who must implement them.

In Why guidelines fail: on consequences of the false dichotomy between global and local knowledge in health systems, he dissects how the separation of “thinkers” (global experts) and “doers” (local staff) dooms many initiatives.

He offers concrete examples of how to close this gap, such as in The Nigeria Immunization Collaborative: Early learning from a novel sector-wide approach model for zero-dose challenges, where thousands of health workers used peer learning to identify root causes of vaccine inequity that central planners had missed.

This theme emphasizes that the solution is not more “technical assistance” from the outside, but better mechanisms to unlock the problem-solving capacity that already exists within communities.

Beyond learning: a new operating system in global development

Taken together, these themes provide the specifications for a new operating system in global development, one that moves beyond the limitations of the models of today.

• Sadki’s work challenges the sector to recognize its most undervalued asset: the collective intelligence of the health and humanitarian workforce.
• By dismantling the barriers between the “high ground” of policy and the “swampy lowlands” of practice, his framework constructs a learning ecosystem where artificial intelligence amplifies human connection and local insights continuously refine global strategy.
• This evolution—from episodic workshops to continuous, networked problem-solving—offers a pragmatic path to close the persistent gap between investment and outcome.

In a resource-constrained world, unlocking this latent capacity is not merely an ethical choice, but a strategic imperative to build systems resilient enough for an unpredictable future.

It's live! 🎉 My latest article, "Note-Taking vs. Personal Knowledge Management: Why Your Brain Will Thank You," is now published.

Dive into the fundamental differences between basic note-taking and a robust PKM system. Discover why adopting a PKM is crucial for your cognitive abilities, particularly as AI advances.

Read it here: https://www.ctnet.co.uk/note-taking-vs-personal-knowledge-management-why-your-brain-will-thank-you/

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What is networked learning?

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

5 key features of networked learning:

Why networked learning matters for health work:

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

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

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

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

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

#globalHealth #learningStrategy #networkedLearning

What is a complex problem?

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

Problems can be simple or complex.

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

Complex problems do not have a single right answer.

They have many possible answers or no answer at all.

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

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

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

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

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

You have to keep adapting your ideas.

To solve complex problems, you need to be able to:

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

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

Image: The Geneva Learning Foundation Collection © 2024

References

#complexLearning #complexProblems #learningStrategy #pedagogy #wickedProblems

Why answer Teach to Reach Questions?

Have you ever wished you could talk to another health worker who has faced the same challenges as you? Someone who found a way to keep helping people, even when things seemed impossible? That’s exactly the kind of active learning that Teach to Reach Questions make possible. They make peer learning easy for everyone who works for health.

What are Teach to Reach Questions?

Once you join Teach to Reach (what is it?), you’ll receive questions about real-world challenges that matter to health professionals.

How does it work?

What’s different about these questions?

Unlike typical surveys that just collect data, Teach to Reach Questions are active learning that:

• Focus on your real-world experience.
• Help you reflect on what worked (and what didn’t).
• Connect you to solutions from other health workers.
• Give back everything shared to help everyone learn.

See what we give back to the community. Get the English-language collection of Experiences shared from Teach to Reach 10. The new compendium includes over 600 health worker experiences about immunisation, climate change, malaria, NTDs, and digital health. A second collection of more than 600 experiences shared by French-speaking participants is also available.

What’s in it for you?

Peer learning happens when we learn from each other. Your answers can help others – and their answers can help you.

A health worker’s experience

Here is what on community health worker from Kenya said:

“When flooding hit our area, I felt so alone trying to figure out how to keep helping people. Through Teach to Reach, I learned that a colleague in another country had faced the same problem. Their solution helped me prepare better for the next flood. Now I’m sharing my experience to help others.”

Think about how peer learning could help you when more than 23,000 health professionals are asked to share their experience on a challenge that matters to you.

Ready to start?

Remember: Your experience, no matter how small it might seem to you, could be exactly what another health worker needs to hear.

The sooner you join, the more you’ll learn from colleagues worldwide.

Together, we can turn what each of us knows into knowledge that helps everyone.

Listen to the Teach to Reach podcast:

• in English on RSS , Apple, Spotify, YouTube or Amazon podcast services.
• En français sur les services de podcast par RSS, Apple, Spotify, YouTube ou Amazon.

Is your organisation interested in learning from health workers? Learn more about becoming a Teach to Reach partner.

Image: The Geneva Learning Foundation Collection © 2024

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#continuousLearning #experientialLearning #fieldBasdLearning #healthWorkers #learningCulture #learningStrategy #methodology #pedagogy #peerLearning #TeachToReach #TeachToReachQuestions #TheGenevaLearningFoundation

In a rural health center in Kenya, a community health worker develops an innovative approach to reaching families who have been hesitant about vaccination.

Meanwhile, in a Brazilian city, a nurse has gotten everyone involved – including families and communities – onboard to integrate information about HPV vaccination into cervical cancer screening.

These valuable insights might once have remained isolated, their potential impact limited to their immediate contexts.

But through Teach to Reach – a peer learning platform, network, and community hosted by The Geneva Learning Foundation – these experiences become part of a larger tapestry of knowledge that transforms how health workers learn and adapt their practices worldwide.

Since January 2021, the event series has grown to connect over 21,000 health professionals from more than 70 countries, reaching its tenth edition with 21,398 participants in June 2024.

Scale matters, but this level of engagement begs the question: how and why does it work?

The challenge in global health is not just about what people need to learn – it is about reimagining how learning happens and gets applied in complex, rapidly-changing environments to improve performance, improve health outcomes, and prepare the next generation of leaders.

Traditional approaches to professional development, built around expert-led training and top-down knowledge transfer, often fail to create lasting change.

They tend to ignore the rich knowledge that exists in practice – what we know when we are there every day, side-by-side with the community we serve – and the complex ways that learning actually occurs in professional networks and communities.

Teach to Reach is one component in The Geneva Learning Foundation’s emergent model for learning and change.

This article describes the pedagogical patterns that Teach to Reach brings to life.

A new vision for digital-first, networked professional learning

Teach to Reach represents a shift in how we think about professional learning in global health.

Its pedagogical pattern draws from three complementary theoretical frameworks that together create a more complete understanding of how professionals learn and how that learning translates into improved practice.

At its foundation lies Bill Cope’s and Mary Kalantzis’s New Learning framework, which recognizes that knowledge creation in the digital age requires new approaches to learning and assessment.

Teach to Reach then integrates insights from Watkins and Marsick’s research on the strong relationship between learning culture (a measure of the capacity for change) and performance and George Siemens’s learning theory of connectivism to create something syncretic: a learning approach that simultaneously builds individual capability, organizational capacity, and network strength.

Active knowledge making

The prevailing model of professional development often treats learners as empty vessels to be filled with expert knowledge.

Drawing from constructivist learning theory, it positions health workers as knowledge creators rather than passive recipients.

When a community health worker in Kenya shares how they’ve adapted vaccination strategies for remote communities, they are not just describing their work – they’re creating valuable knowledge that others can learn from and adapt.

The role of experts is even more significant in this model: experts become “Guides on the side”, listening to challenges and their contexts to identify what expert knowledge is most likely to be useful to a specific challenge and context.

(This is the oft-neglected “downstream” to the “upstream” work that goes into the creation of global guidelines.)

This principle manifests in how questions are framed.

Instead of asking “What should you do when faced with vaccine hesitancy?” Teach to Reach asks “Tell us about a time when you successfully addressed vaccine hesitancy in your community.” This subtle shift transforms the learning dynamic from theoretical to practical, from passive to active.

Collaborative intelligence

The concept of collaborative intelligence, inspired by social learning theory, recognizes that knowledge in complex fields like global health is distributed across many individuals and contexts.

No single expert or institution holds all the answers.

By creating structures for health workers to share and learn from each other’s experiences, Teach to Reach taps into what cognitive scientists call “distributed cognition” – the idea that knowledge and understanding emerge from networks of people rather than individual minds.

This plays out practically in how experiences are shared and synthesized.

When a nurse in Brazil shares their approach to integrating COVID-19 vaccination with routine immunization, their experience becomes part of a larger tapestry of knowledge that includes perspectives from diverse contexts and roles.

Metacognitive reflection

Metacognition – thinking about thinking – is crucial for professional development, yet it is often overlooked in traditional training.

Teach to Reach deliberately builds in opportunities for metacognitive reflection through its question design and response framework.

When participants share experiences, they are prompted not just to describe what happened, but to analyze why they made certain decisions and what they learned from the experience.
This reflective practice helps health workers develop deeper understanding of their own practice and decision-making processes.

It transforms individual experiences into learning opportunities that benefit both the sharer and the wider community.

Recursive feedback

Learning is not linear – it is a cyclical process of sharing, reflecting, applying, and refining.

Teach to Reach’s model of recursive feedback, inspired by systems thinking, creates multiple opportunities for participants to engage with and build upon each other’s experiences.

This goes beyond communities of practice, because the community component is part of a broader, dynamic and ongoing process.

Executing a complex pedagogical pattern

The pedagogical pattern of Teach to Reach come to life through a carefully designed implementation framework over a six-month period, before, during, and after the live event.

This extended timeframe is not arbitrary – it is based on research showing that sustained engagement over time leads to deeper learning and more lasting change than one-off learning events.
The core of the learning process is the Teach to Reach Questions – weekly prompts that guide participants through progressively more complex reflection and sharing.

These questions are crafted to elicit not just information, but insight and understanding.

They follow a deliberate sequence that moves from description to analysis to reflection to application, mirroring the natural cycle of experiential learning.

Communication as pedagogy

In Teach to Reach, communication is not just about delivering information – it is an integral part of the learning process.

The model uses what scholars call “pedagogical communication” – communication designed specifically to facilitate learning.

This manifests in several ways:

• Personal and warm tone that creates psychological safety for sharing
• Clear calls to action that guide participants through the learning process
• Multiple touchpoints that reinforce learning and maintain engagement
• Progressive engagement that builds complexity gradually

Learning culture and performance

Watkins and Marsick’s work helps us understand why Teach to Reach’s approach is so effective.

Learning culture – the set of organizational values, practices, and systems that support continuous learning – is crucial for translating individual insights into improved organizational performance.

Teach to Reach deliberately builds elements of strong learning cultures into its design.

Furthermore, the Geneva Learning Foundation’s research found that continuous learning is the weakest dimension of learning culture in immunization – and probably global health.

Hence, Teach to Reach itself provides a mechanism to strengthen specifically this dimension.

Take the simple act of asking questions about real work experiences.

This is not just about gathering information – it’s about creating what Watkins and Marsick call “inquiry and dialogue,” a fundamental dimension of learning organizations.

When health workers share their experiences, they are not just describing what happened.

They are engaging in a form of collaborative inquiry that helps everyone involved develop deeper understanding.

Networks of knowledge

George Siemens’s connectivism theory provides another crucial lens for understanding Teach to Reach’s effectiveness.

In today’s world, knowledge is not just what is in our heads – it is distributed across networks of people and resources.

Teach to Reach creates and strengthens these networks through its unique approach to asynchronous peer learning.

The process begins with carefully designed questions that prompt health workers to share specific experiences.

But it does not stop there.

These experiences become nodes in a growing network of knowledge, connected through themes, challenges, and solutions.

When a health worker in India reads about how a colleague in Nigeria addressed a particular challenge, they are not just learning about one solution – they are becoming part of a network that makes everyone’s practice stronger.

From theory to practice

What makes Teach to Reach particularly powerful is how it fuses multiple theories of learning into a practical model that works in real-world conditions.

The model recognizes that learning must be accessible to health workers dealing with limited connectivity, heavy workloads, and diverse linguistic and cultural contexts.

New Learning’s emphasis on multimodal meaning-making supports the use of multiple communication channels ensuring accessibility.

Learning culture principles guide the creation of supportive structures that make continuous learning possible even in challenging conditions.

Connectivist insights inform how knowledge is shared and distributed across the network.

Creating sustainable change

The real test of any learning approach is whether it creates sustainable change in practice.

By simultaneously building individual capability, organizational capacity, and network strength, it creates the conditions for continuous improvement and adaptation.

Health workers do not just learn new approaches – they develop the capacity to learn continuously from their own experience and the experiences of others.

Organizations do not just gain new knowledge – they develop stronger learning cultures that support ongoing innovation.

And the broader health system gains not just a collection of good practices, but a living network of practitioners who continue to learn and adapt together.

Looking forward

As global health challenges have become more complex, the need for more effective approaches to professional learning becomes more urgent.

Teach to Reach’s pedagogical model, grounded in complementary theoretical frameworks and proven in practice, offers valuable insights for anyone interested in creating impactful professional learning experiences.

The model suggests that effective professional learning in complex fields like global health requires more than just good content or engaging delivery.

It requires careful attention to how learning cultures are built, how networks are strengthened, and how individual learning connects to organizational and system performance.

Most importantly, it reminds us that the most powerful learning often happens not through traditional training but through thoughtfully structured opportunities for professionals to learn from and with each other.

In this way, Teach to Reach is a demonstration of what becomes possible when we reimagine how professional learning happens in service of better health outcomes worldwide.

Image: The Geneva Learning Foundation Collection © 2024

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