The limitations of expert-led fellowships for global health

How to overcome limitations of expert-led fellowships for global health

Reda SadkiGlobal health, Theory

Coaching and mentoring programs sometimes called “fellowships” have been upheld as the gold standard for developing leaders in global health.

For example, a fellowship in the field of immunization was recently advertised in the following manner.

  • Develop your skills and become an advocate and leader: The fellowship will begin with two months of weekly mandatory live engagements led by [global] staff and immunization experts around topics relating to rebuilding routine immunization, including catch-up vaccination, integration and life course immunization. […]
  • Craft an implementation plan: Throughout the live engagement series, fellows will develop, revise and submit a COVID-19 recovery strategic plan.
  • Receive individualized mentoring: Participants with strong plans will be considered for a mentorship program to work 1:1 with experts in the field to further develop and implement their strategies and potentially publish their case studies.

We will not dwell here on the ‘live engagements’, which are expert-led presentations of technical knowledge. We already know that such ‘webinars’ have very limited learning efficacy, and unlikely impact on outcomes. (This may seem like a harsh statement to global health practitioners who have grown comfortable with webinars, but it is substantiated by decades of evidence from learning science research.)

On the surface, the rest of the model sounds highly effective, promising personalized attention and expert guidance.

The use of a project-based learning approach is promising, but it is unclear what support is provided once the implementation plan has been crafted.

It is when you consider the logistical aspects that the cracks begin to show.

The essence of traditional coaching lies in the quality of the one-to-one interaction, making it an inherently limited resource.

Take, for example, a fellowship programme where interest outstrips availability—say, 1,600 aspiring global health leaders are interested, but only 30 will be selected for one-on-one mentoring.

Tailored, one-on-one coaching can be incredibly effective in small, controlled environments.

While these 30 may receive an invaluable experience, what happens to those left behind?

There is an ‘elitist spiral’.

Coaching and mentoring, while intensive, remain exclusive by design, limited to the select few.

This not only restricts scale but also concentrates knowledge among the selected group, perpetuating hierarchies.

Those chosen gain invaluable support.

The majority left out are denied access and implicitly viewed as passive recipients rather than partners in a collective solution.

Doubling the number of ‘fellows’ only marginally improves this situation.

Even if the mentor pool were to grow exponentially, the personalized nature of the engagement limits the rate of diffusion.

When we step back and look at the big picture, we realize there is a problem: these programs are expensive and difficult to scale.

And, in global health, if it does not scale, it is not solving the problem.

How does the scalability of peer learning compare to expert-led coaching ‘fellowships’?

Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

So while these programs can make a real difference for a small group of people, they are unlikely to move the needle on a global scale.

That is like trying to fill a swimming pool with a teaspoon—you might make some progress, but you will never get the job done.

The model creates a paradox: the attributes making it effective for individuals intrinsically limit systemic impact.

There is another paradox related to complexity.

Global health issues are inextricably tied to cultural, political and economic factors unique to each country and community.

Complex problems require nuanced solutions.

Yet coaching promotes generalized expertise from a few global, centralized institutions rather than fostering context-specific knowledge.

Even the most brilliant, experienced coach or mentor cannot single-handedly impart the multifaceted understanding needed to drive impact across diverse settings.

A ‘fellowship’ structure also subtly perpetuates the existing hierarchies within global health.

It operates on the tacit assumption that the necessary knowledge and expertise reside in certain centralized locations and among a select cadre of experts.

This sends an implicit message that knowledge flows unidirectionally—from the seasoned experts to the less-experienced practitioners who are perceived as needing to be “coached.”

Learn more: How does peer learning compare to expert-led coaching ‘fellowships’?

Peer learning: Collective wisdom, collective progress

In global health, no one individual or institution can be expected to possess solutions for all settings.

Sustainable change requires mobilizing collective intelligence, not just centralized expertise.

Learn more: The COVID-19 Peer Hub as an example of Collective Intelligence (CI) in practice

This means transitioning from hierarchical, top-down development models to flexible platforms amplifying practitioners’ contextual insights.

The gap between need and availability of quality training in global health is too vast for conventional approaches to ever bridge alone.

Instead of desperately chasing an asymptote of expanding elite access, we stand to gain more by embracing approaches that democratize development.

Complex challenges demand platforms unleashing collective wisdom through collaboration. The technologies exist.

In the “fellowship” example, less than five percent of participants were selected to receive feedback from global experts.

A peer learning platform can provide high-quality peer feedback for everyone.

  • Such a platform democratizes access to knowledge and disrupts traditional hierarchies.
  • It also moves away from the outdated notion that expertise is concentrated in specific geographical or institutional locations.

What learning science underpins peer learning for global health? Watch this 14-minute presentation at the 2023 annual meeting of the American Society for Tropical Medicine and Hygiene (ASTMH).

What about the perceived trade-off between quality and scale?

Effective digital peer learning platforms negate this zero-sum game.

Research on MOOCs (massive open online courses) has conclusively demonstrated that giving and receiving feedback to and from three peers through structured, rubric-based peer review, achieves reliability comparable, when properly supported, to that of expert feedback alone.

If we are going to make a dent in the global health crises we face, we have to shift from a model that relies on the expertise of the few to one that harnesses the collective wisdom of the many.

  • Peer learning isn’t a Band-Aid. It is an innovative leap forward that disrupts the status quo, and it’s exactly what the global health sector needs.
  • Peer learning is not just an incremental improvement. It is a seismic shift in the way we think about learning and capacity-building in global health.
  • Peer learning is not a compromise. It is an upgrade. We move from a model of scarcity, bound by the limits of individual expertise, to one of collective wisdom.
  • Peer learning is more than just a useful tool. It is a challenge to the traditional epistemology of global health education.

Read about a practical example: Movement for Immunization Agenda 2030 (IA2030): grounding action in local realities to reach the unreached

As we grapple with urgent issues in global health—from pandemic recovery to routine immunization—it is clear that we need collective intelligence and resource sharing on a massive scale.

And for that, we need to move beyond the selective, top-down models of the past.

The collective challenges we face in global health require collective solutions.

And collective solutions require us to question established norms, particularly when those norms serve to maintain existing hierarchies and power imbalances.

Now it is up to us to seize this opportunity and move beyond outmoded, hierarchical models.

There is a path – now, not tomorrow – to truly democratize knowledge, make meaningful progress, and tackle the global health challenges that confront us all.

How does peer learning compare to expert-led coaching fellowships

How does the scalability of peer learning compare to expert-led coaching ‘fellowships’?

Reda SadkiGlobal health, Theory

By connecting practitioners to learn from each other, peer learning facilitates collaborative development. ow does it compare to expert-led coaching and mentoring “fellowships” that are seen as the ‘gold standard’ for professional development in global health?

Scalability in global health matters. (See this article for a comparison of other aspects.)

Simplified mathematical modeling can compare the scalability of expert coaching (“fellowships”) and peer learning

Let N be the total number of learners and M be the number of experts available. Assuming that each expert can coach K learners effectively:

\text{Total Number of Coached Learners} = M \times K

For N>>M×KN>>M×K, it is evident that expert coaching is costly and difficult to scale.

Expert coaching “fellowships” require the availability of experts, which is often optimistic in highly specialized fields.

The number of learners (N) greatly exceeds the product of the number of experts (M) and the capacity per expert (K).

Scalability of one-to-one peer learning

By comparison, peer learning turns the conventional model on its head by transforming each learner into a potential coach who can provide peer feedback.

This has significant advantages in scalability.

Let N be the total number of learners. Assuming a peer-to-peer model, where each learner can learn from any other learner:

\text{Total Number of Learning Interactions} = \frac{N \times (N - 1)}{2}

\text{The number of learning interactions scales with: } O(N^2)

In this context, the number of learning interactions scales quadratically with the number of learners. This means that if the number of learners doubles, the total number of learning interactions increases by a factor of four. This quadratic relationship highlights the significant increase in interactions (and potential scalability challenges) as more learners participate in the model.

However, this one-to-one model is difficult to implement: not every learner is going to interact with every other learner in meaningful ways.

A more practical ‘triangular’ peer learning model with no upper limit to scalability

In The Geneva Learning Foundation’s peer learning model, learners give feedback to three peers, and receive feedback from three peers. This is a structured, time-bound process of peer review, guided by an expert-designed rubric.

When each learner gives feedback to 3 different learners and receives feedback from 3 different learners, the model changes significantly from the one-to-one model where every learner could potentially interact with every other learner. In this specific configuration, the total number of interactions can be calculated based on the number of learners N, with each learner being involved in 6 interactions (3 given + 3 received).

The total number of interactions per learner is six. However, since each interaction involves two learners (the giver and the receiver of feedback), we do not need to double-count these interactions for the total count in the system. Hence, the total number of interactions for each learner is directly 6, without further adjustments for double-counting.

Therefore, the total number of learning interactions in the system can be represented as:

\text{Total Number of Learning Interactions} = N \times 3

Given this setup, the complexity or scalability of the system in terms of learning interactions relative to the number of participants N is linear. This is because the total number of interactions increases directly in proportion to the number of learners. Thus, the Big O notation would be:

O(N)

This indicates that the total number of learning interactions scales linearly with the number of learners. In this configuration, as the number of learners increases, the total number of interactions increases at a linear rate, which is more scalable and manageable than the quadratic rate seen in the peer-to-peer model where every learner interacts with every other learner. Learn more: There is no scale.

Illustration: The Geneva Learning Foundation © 2024

Teach to Reach 10 climate change and health

Become a Teach to Reach 10 Partner: Help amplify frontline voices at the world’s largest health peer learning event

Reda SadkiEvents, The Geneva Learning Foundation

The Geneva Learning Foundation is pleased to announce the tenth edition of Teach to Reach, to be held 20-21 June 2024.

Teach to Reach is a massive, open peer learning event where health professionals network, and learn with colleagues from all over the world. Request your invitation…

Teach to Reach 10 continues a tradition of groundbreaking peer learning started in 2020, when over 3,000 health workers from 80 countries came together to improve immunization training.

17,662 health professionals – over 80% from districts and facilities, half working for government – participated in Teach to Reach 9 in October 2023. Participants shared 940 experiences ahead of the event. See what we learned at Teach to Reach 9 or view Insights Live with Dr Orin Levine.

Teach to Reach is a platform, community, and network to amplify voices from lower-resource settings bearing the greatest burden of disease.

Teach to Reach 10 will focus on the impacts of climate change on health, following the publication of a ground-breaking report sharing insights of over 1,200 health workers.

In the video below, learn from the experiences of 4,700 participants in our Special Event: From community to planet: Health professionals on the frontlines of climate change.

Poor connectivity? You will find the videos on this page in the low-bandwidth, audio-only Teach to Reach podcast on Apple, Spotify, Google, or Amazon Podcasts.

Alongside this theme, other critical health challenges selected by participants for this tenth edition include the Movement for Immunization Agenda 2030 (IA2030), neglected tropical diseases (NTDs), and neglected needs of women’s health.

In this video of a Teach to Reach session, learn about local action led by community-based health workers to tackle Female Genital Schistosomiasis (FGS), a neglected tropical disease that affects an estimated 56 million women and girls.

In the run-up to Teach to Reach 10, participants will share their real-world experience. Every success, lessons learned, and challenge will be shared back with the community and brought to the attention of partners.

The Manifesto for investment in health workers, a visionary statement elaborated by over 1,300 health workers, will be launched at Teach to Reach 10.

A diverse range of over 50 global organizations have partnered with Teach to Reach since 2020, including Gavi the Vaccine Alliance, the Wellcome Trust, and UNICEF.

The next video is a session with UNICEF on reaching zero-dose children in urban settings.

Alongside global partners and ministries of health, local community-based organizations will also be invited to become Teach to Reach partners.

Partners are invited to join the first Partner Briefing on Monday 4 March 2024, bringing together global health organizations with a commitment to listening and learning from health workers and the communities they serve.

Illustration: The Geneva Learning Foundation © 2024

Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

Reda SadkiGlobal health, Theory

A formula for calculating learning efficacy, (E), considering the importance of each criterion and the specific ratings for peer learning, is:

\text{Efficacy} = \frac{S \cdot w_S + I \cdot w_I + C \cdot w_C + F \cdot w_F + U \cdot w_U}{w_S + w_I + w_C + w_F + w_U}

This abstract formula provides a way to quantify learning efficacy, considering various educational criteria and their relative importance (weights) for effective learning.

Variable DefinitionDescription 
SScalabilityAbility to accommodate a large number of learners 
IInformation fidelityQuality and reliability of information 
CCost effectivenessFinancial efficiency of the learning method 
FFeedback qualityQuality of feedback received 
UUniformityConsistency of learning experience 
Summary of five variables that contribute to learning efficacy

Weights for each variables are derived from empirical data and expert consensus.

All values are on a scale of 0-4, with a “4” representing the highest level.

ScalabilityInformation fidelityCost-benefitFeedback qualityUniformity
w_Sw_Iw_Cw_Fw_U
4.003.004.003.001.00
Assigned weights

Here is a summary table including all values for each criterion, learning efficacy calculated with weights, and Efficacy-Scale Score (ESS) for peer learning, cascade training, and expert coaching.

The Efficacy-Scale Score (ESS) can be calculated by multiplying the efficacy (E) of a learning method by the number of learners (N).

\text{ESS} = E \times N

This table provides a detailed comparison of the values for each criterion across the different learning methods, the calculated learning efficacy values considering the specified weights, and the Efficacy-Scale Score (ESS) for each method.

Type of learningScalabilityInformation fidelityCost effectivenessFeedback qualityUniformityLearning efficacy# of learnersEfficacy-Scale Score
Peer learning4.002.504.002.501.003.2010003200
Cascade training2.001.002.000.500.501.40500700
Expert coaching0.504.001.004.003.002.2060132

Of course, there are many nuances in individual programmes that could affect the real-world effectiveness of this simple model. The model, grounded in empirical data and simplified to highlight core determinants of learning efficacy, leverages statistical weighting to prioritize key educational factors, acknowledging its abstraction from the multifaceted nature of educational effectiveness and assumptions may not capture all nuances of individual learning scenarios.

Peer learning

The calculated learning efficacy for peer learning, (E_{\text {peer}}) , is 3.20. This value reflects the weighted assessment of peer learning’s strengths and characteristics according to the provided criteria and their importance.

By virtue of scalability, ESS for peer learning is 24 times higher than expert coaching.

Cascade training

For Cascade Training, the calculated learning efficacy, (E_{\text {cascade}}), is approximately 1.40. This reflects the weighted assessment based on the provided criteria and their importance, indicating lower efficacy compared to peer learning.

Cascade training has a higher ESS than expert coaching, due to its ability to achieve scale.

Learn more: Why does cascade training fail?

Expert coaching

For Expert Coaching, the calculated learning efficacy, (E_{\text {expert}}), is approximately 2.20. This value indicates higher efficacy than cascade training but lower than peer learning.

However, the ESS is the lowest of the three methods, primarily due to its inability to scale. Read this article for a scalability comparison between expert coaching and peer learning.

Image: The Geneva Learning Foundation Collection © 2024

Why does cascade training fail

Why does cascade training fail?

Reda SadkiGlobal health, Theory

Cascade training remains widely used in global health.

Cascade training can look great on paper: an expert trains a small group who, in turn, train others, thereby theoretically scaling the knowledge across an organization.

It attempts to combine the advantages of expert coaching and peer learning by passing knowledge down a hierarchy.

However, despite its promise and persistent use, cascade training is plagued by several factors that often lead to its failure.

This is well-documented in the field of learning, but largely unknown (or ignored) in global health.

What are the mechanics of this known inefficacy?

Here are four factors that contribute to the failure of cascade training

1. Information loss

Consider a model where an expert holds a knowledge set K. In each subsequent layer of the cascade, α percentage of the knowledge is lost:

K_n = K \cdot \alpha^n

  • Where K_n is the knowledge at the nth level of the cascade. As n grows, K_n exponentially decreases, leading to severe information loss.
  • Each layer in the cascade introduces a potential for misunderstanding the original information, leading to the training equivalent of the ‘telephone game’.

2. Lack of feedback

In a cascade model, only the first layer receives feedback from an actual expert.

  • Subsequent layers have to rely on their immediate ‘trainers,’ who might not have the expertise to correct nuanced mistakes.
  • The hierarchical relationship between trainer and trainee is different from peer learning, in which it is assumed that everyone has something to learn from others, and expertise is produced through collaborative learning.

3. Skill variation

  • Not everyone is equipped to teach others.
  • The people who receive the training first are not necessarily the best at conveying it to the next layer, leading to unequal training quality.

4. Dilution of responsibility

  • As the cascade flows down, the sense of responsibility for the quality and fidelity of the training dilutes.
  • The absence of feedback to drive a quality development process exacerbates this.

Image: The Geneva Learning Foundation Collection © 2024

The capability trap

The capability trap: Nobody ever gets credit for fixing problems that never happened

Reda SadkiLeadership, Learning strategy

Here is a summary of the key points about the capability trap, from the article “Nobody ever gets credit for fixing problems that never happened: creating and sustaining process improvement”.

What is the capability trap?

  • Many companies invest heavily in process improvement programs, yet few efforts actually produce significant results. This is called the “improvement paradox”.
  • The problem lies not with the specific tools, but rather how the introduction of new programs interacts with existing organizational structures and dynamics.
  • Using system dynamics modeling, the authors studied implementation challenges in depth through over a dozen case studies. Their models reveal insights into why improvement programs often fail.

Core causal loops

  • The “Work Harder” loop – managers pressure people to spend more time working to immediately boost throughput and close performance gaps. But this is only temporary.
  • The “Work Smarter” loop – managers encourage improvement activities which enhance process capability over time for more enduring gains, but there is a delay before benefits are seen.
  • The “Reinvestment” reinforcing loop – successfully improving capability frees up more time for further improvement. But the reverse vicious cycle often dominates instead.
  • The “Shortcuts” loop – facing pressure, people cut corners on improvement activities which temporarily frees up more time for work. But this gradually erodes capability.

The capability trap

  • Short-term “Work Harder” and “Shortcuts” decisions eventually hurt capability and require heroic work efforts to maintain performance, creating a downward spiral.
  • However, because capability erodes slowly, managers fail to connect problems to past decisions and blame poor worker motivation instead, leading to a self-confirming cycle.
  • Even improvement programs just increase pressure and drive more shortcuts, making stereotypes and conflicts worse. This “capability trap” causes programs to fail.

The “capability trap” refers to the downward spiral organizations can get caught in, where attempting to boost performance by pressuring people to “work harder” actually erodes process capability over time. This trap works through a few key mechanisms:

  1. Facing pressure, people cut corners and reduce time spent on improvement activities in order to free up more time for immediate work. This temporarily boosts throughput.
  2. However, this comes at a cost of gradually declining process capability, as less time is invested in maintenance, training, and problem solving.
  3. Capability erosion then reduces performance, widening the gap versus desired performance levels.
  4. Managers falsely attribute this to poor motivation or effort from the workforce. They lack awareness of the capability trap dynamics, and the delays between pressing people to “work harder” and the capability declines that eventually ensue.
  5. Management increases pressure further, demanding heroic work efforts, which causes workers to cut even more corners. This spirals capability downward while confirming management’s incorrect attribution even more.

Key takeaway for learning leaders

Learning leaders must understand the systemic traps identified in the article that underly failed improvement initiatives and facilitate mental model shifts. This help build sustainable, effective learning programs to be realized through productive capability-enhancing cycles.

Key takeaway for immunization leaders

It is reasonable to hypothesize that poor health worker performance is a symptom rather than the cause of poor immunization programme performance. Short-term decisions, often responding to top-down targets and donor requirements, hurt capability and require, as the authors say, “heroic work efforts to maintain performance, creating a downward spiral.” Managers then incorrectly diagnose this as a performance problem due to motivation.

How to escape the capability trap

The key to avoiding or escaping this trap is therefore shifting the mental models that reinforce the incorrect attributions about motivation. Some ways to do this include:

  • Educating managers on the systemic structures causing the capability trap through methods like system dynamics modeling
  • Allowing time for capability-enhancing improvements to take effect before judging performance
  • Incentivizing quality and sustainability of throughput rather than just short-term volume alone
  • Seeking input from workers on the barriers to improvement they face

With awareness of the structural causes and delays, managers can avoid erroneously attributing blame. Patience and a systems perspective are critical for companies to invest their way out of the capability trap.

  • Shift mental models to recognize system structures leading to the capability trap, rather than blaming people. Then improvement tools can work.
  • A useful example could be system dynamics workshops that achieved this shift and enabled successful programs, dramatically enhancing performance.

Reference

Repenning, N.P., Sterman, J.D., 2001. Nobody ever gets credit for fixing problems that never happened: creating and sustaining process improvement. California management review 43, 64–88. https://doi.org/10.2307/41166101

Illustration: The Geneva Learning Foundation Collection © 2024

Gender analysis of the World Health Organization online learning program on Immunization Agenda 2030

Gender analysis of the World Health Organization online learning program on Immunization Agenda 2030

Reda SadkiGlobal health

The article “Gender analysis of the World Health Organization online learning program on Immunization Agenda 2030” is, according to the authors, “the first to showcase the positive inclusion of mainstreaming gender in a WHO capacity-building program.”

Context:

  • The paper analyzes action plans developed and peer reviewed by participants in one cohort of the 2021 World Health Organization (WHO) Scholar Level 1 certification course on Immunization Agenda 2030 (IA2030), a course developed by The Geneva Learning Foundation (TGLF) with funding from the Bill & Melinda Gates Foundation (BMGF).
  • WHO’s Scholar courses only utilize the knowledge creation component of TGLF’s learning-to-action model, whereas the full model supports implementation that leads to improved health outcomes.
  • TGLF uses an innovative peer learning-to-action model, developed through over a decade of research and practice, focused on knowledge creation through dialogue, critique, and collaboration, with rubric-based peer feedback scaffolding the learning process.
  • The course was facilitated by Charlotte Mbuh and Min Zha, two women learning leaders at The Geneva Learning Foundation (TGLF), who combine deep expertise in learning science and real-world knowledge of immunization in low- and middle-income countries (LMICs).

Key findings:

  • The analysis included 111 action plans, a subset of the projects and insights shared, from participants across 31 countries working to improve immunization programs.
  • It found that “all action plans in the 111 sample, except three, included gender considerations” showing the course was effective in raising awareness of gender barriers.

This is consistent with the known effectiveness of peer feedback, as the rubric followed by each learner included specific instructions to “describe how your action plan has considered and integrated gender dimensions in immunization.”

TGLF’s peer learning model focuses on generating and applying new knowledge. This appears to be conducive to raising awareness of issues like gender barriers to immunization. By giving and receiving feedback, participants build understanding.

Whereas only around ten percent of learners participated in expert-led presentations offered about gender and immunization, every learner had to think through and write up gender analysis. And every learner had to give feedback on the gender analyses of three colleagues.

The social nature of giving and received structured peer feedback, supported by expert-designed resources, creates accountability and motivation for integrating gender considerations. Participants educate one another on blindspots, helping embed attention to gender issues.

Compared to traditional expert-led capacity building, this peer-led approach empowered participants to learn from each other’s experience, situating gender in their real-world practice, rather than as an abstract concept that requires global experts to explain it. This participant-driven process with built-in feedback mechanisms is likely to have helped make the increased gender awareness actionable.

Gender analysis: what we learned about gender barriers

  • The most cited barrier was “low education and health literacy” affecting immunization uptake. As one plan stated, “lower educational levels of maternal caregivers are more commonly related to under-vaccination”.
  • Other major barriers were difficulties accessing services due to “gender-related factors influencing mobility, location, availability, or quality of health services” and lack of male involvement in decisions, as “men make most of the household decisions while they often do not have sufficient information”.
  • Proposed strategies focused on areas like “incentive schemes” and “on-the-job support” for female health workers, “community engagement” to improve literacy, and better “engagement of men” in immunization activities.

TGLF’s peer learning approach likely contributed to raising awareness of gender issues and ability to propose context-specific solutions, though some implicit biases may have affected peer evaluations.

Overall, the analysis shows mainstreaming gender was an effective part of this capacity building program, and the authors appear convinced of its potential to lead to more gender-equitable and effective immunization policies and services.

However, the authors’ claim that “gender inequality and harmful gender norms in many settings create barriers and are the main reasons for suboptimal immunization coverage” is not substantiated by the available data. The action plans do provide some contextual descriptions of gender barriers and describe an intent to take action. But descriptions shared by learners were not verified, and the course did not offer any support to learners in implementing their proposed actions.

Reference

Nyasulu, B.J., Heidari, S., Manna, M., Bahl, J., Goodman, T., 2023. Gender analysis of the World Health Organization online learning program on Immunization Agenda 2030. Frontiers in Global Women’s Health 4, 1230109. https://doi.org/10.3389/fgwh.2023.1230109

Illustration: The Geneva Learning Foundation Collection © 2024

Towards reimagined technical assistance Thinking beyond the current policy options

Towards reimagined technical assistance: thinking beyond the current policy options

Reda SadkiGlobal health, Leadership

In the article “Towards reimagined technical assistance: the current policy options and opportunities for change”, Alexandra Nastase and her colleagues argues that technical assistance should be framed as a policy option for governments. It outlines different models of technical assistance:

  1. Capacity substitution: Technical advisers perform government functions due to urgent needs or lack of in-house expertise. This can fill gaps but has “clear limitations in building state capability.”
  2. Capacity supplementation: Technical advisers provide specific expertise to complement government efforts in challenging areas. This can “fill essential gaps at critical moments” but has limitations for building sustainable capacity.  
  3. Capacity development: Technical advisers play a facilitator role focused on enabling change and strengthening government capacity over the long term. This takes time but “there is a higher chance that these [results] will be sustainable.”

Governments may choose from this spectrum of roles for technical advisers in designing assistance programs based on the objectives, limitations, and tradeoffs involved with each approach: “The most common fallacy is to expect every type of technical assistance to lead to capacity development. We do not believe that is the case. Suppose governments choose to use externals to do the work and replace government functions. In that case, it is not realistic to expect that it will build a capability to do the work independently of consultants.”

Furthermore, technical assistance should be designed through “meaningful and equal dialogue between governments and funders” to ensure it focuses on core issues and builds sustainable capacity. Considerations that need to be highlighted include balancing short-term needs with long-term capacity building and shifting power to local experts.

However, this requires reframing technical assistance as a policy option through transparent dialogue between government and funders.

What key assumptions about technical assistance does this challenge?

The article challenges some key assumptions and orthodox views about technical assistance in global health:

  1. It frames technical assistance not as aid provided by donors, but as a policy option and domestic choice that governments make to meet their objectives. This contrasts with the common donor-centric view.
  2. It critiques the assumption that all technical assistance inherently builds sustainable government capacity and questions this expected linear relationship. The article argues different types of technical assistance have fundamentally different aims – gap-filling versus long-term capacity building.
  3. The article challenges the idealistic principles often promoted for technical assistance, like localization, government ownership, and adaptability. It suggests the evidence is lacking on if these principles effectively lead to better development outcomes on the ground.  
  4. The article argues that technical assistance decisions involve real dilemmas, tradeoffs and tensions in practice rather than being clear cut. It challenges the notion of win-win solutions and highlights risks like unintended consequences.
  5. By outlining limitations of different technical assistance approaches, the article pushes back against a one-size-fits-all mindset. The appropriate approach depends on contextual factors and clarity of purpose.
  6. The article questions typical measures of success for technical assistance based on fast results and output delivery. It advocates for greater focus on processes that enable long-term capacity development even if slower.

How does The Geneva Learning Foundation’s work fit into such a model?

At The Geneva Learning Foundation (TGLF), we realized that our own model to support locally-led leadership to drive change could be described as a new type of technical assistance that does not fit into any of the existing three categories, because:

  1. TGLF’s model is grounded in principles of localization and decolonization that shift power dynamics by empowering government health workers from all levels of the health system – not only the national authorities – to recognize what change is needed, to lead this change where they work. We have observed that, even in fragile contexts, this accelerates progress toward country goals, and strengthens or can help rebuild civil society fabric.
  2. It focuses on nurturing intrinsic motivation and peer accountability rather than imposing top-down directives or extrinsic incentives. 
  3. It utilizes lateral feedback loops and informal, self-organized networks that cut across hierarchies and geographic boundaries.
  4. It emphasizes flexibility, adaptation to local contexts, and problem-driven iteration rather than pre-defined solutions.
  5. It builds sustainable capacity and self-organized learning cultures that reduce dependency on external support.

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

Illustration: The Geneva Learning Foundation Collection © 2024

Protect Invest Together

Protect, invest, together: strengthening health workforce through new learning models

Reda SadkiGlobal health

In “Prioritising the health and care workforce shortage: protect, invest, together,” Agyeman-Manu et al. assert that the COVID-19 pandemic aggravated longstanding health workforce deficiencies globally, especially in under-resourced nations. 

With projected shortages of 10 million health workers concentrated in Africa and the Middle East by 2030, the authors urgently call for policymakers to commit to retaining and expanding national health workforces. 

They propose common-sense solutions: increased, coordinated financing and collaboration across government agencies managing health, finance, economic development, education and labor portfolios.

But how can such interconnected, long-term investments be designed for maximum sustainable impact?

And what is the role of education?

Rethinking health worker learning

In a 2021 WHO survey across 159 countries, most health workers reported lacking adequate training to respond effectively to pandemic demands. This exposed systemic weaknesses in how health workforces develop skills at scale. Long before the COVID-19 pandemic, limitations of traditional learning approaches were already obvious.

Prevailing modalities overly rely on passive knowledge transfer rather than active learner empowerment and engagement with real-world complexities. While assessment and credentialing are important, ultimately learning must be judged by its relevance, application and impact on people’s lives and health systems.

Between April and June 2020, I had the privilege of working with a group of 600 of Scholars of The Geneva Learning Foundation (TGLF) from 86 countries. Together, we designed an immersive learning cycle integrating skill-building and peer exchange for those on the frontlines of the epidemic. We called it the “COVID-19 Peer Hub”. 

It grew into an ecosystem that connected over 6,000 health professionals across 86 countries to share unfiltered insights, give voice to on-the-ground needs, and turn shared experience into action.

Within three months, a third of participants had already implemented COVID-19 recovery plans, citing peer support as the main driver for turning their commitment into results.

By the end of 2020, TGLF’s immunization platform, network, and community had tripled in size.

In 2022, this network transformed into a Movement for Immunization Agenda 2030 (IA2030).

Informing health workforce decisions

What insights can health workforce policymakers draw from the Geneva Learning Foundation’s unique work to achieve the ambitious growth and support targets outlined by Agyeman-Manu et al.?

First, expert-driven, top-down  approaches alone cannot handle emergent real-world complexities. In TGLF’s learning cycles, the most significant learning often occurs in lateral, one-to-one networking meetings between peers. These defy boundaries of geography, gender, ethnicity, religion, and job roles.

Second, thoughtfully-applied technology can exponentially accelerate learning’s reach, access and connections following learner needs. New digital modalities opened by pandemic disruptions must be sustained and optimized post-crisis, despite the tendency to revert back to previous norms of learning through high-cost, low-volume formal trainings and workshop.

Third, relevance heightens learning and application. Learning and teaching should not just be centered on learners’ needs and problems to boost motivation and effectiveness. Learning cannot be detached from its context.

Finally, nurturing cultures that support effective learning matters for performance and human achievement. Systems enabling peer reward and accountability build resilience.

Protect, invest, together in a learning workforce

Health policymakers are manifesting intent to act on the health workforce crisis.

Alongside urgent investments, applying systemic perspectives from learning innovations like those The Geneva Learning Foundation has pioneered presents a path to growing motivated, capable workforces ready for the challenges ahead.

Rethinking assumptions opens eyes – when we commit to support health workers holistically, the rewards radiate across health ecosystems.

Reference: Agyeman-Manu et al. Prioritising the health and care workforce shortage: protect, invest, together. The Lancet Global Health (2023). https://doi.org/10.1016/S2214-109X(23)00224-3

The imperative for climate action to protect health and the role of education

The imperative for climate action to protect health and the role of education

Reda SadkiGlobal health

“The Imperative for Climate Action to Protect Health” is an article that examines the current and projected health impacts of climate change, as well as the potential health benefits of actions to reduce greenhouse gas emissions. The authors state that “climate change is causing injuries, illnesses, and deaths, with the risks projected to increase substantially with additional climate change.” 

Specifically, the article notes that approximately “250,000 deaths annually between 2030 and 2050 could be due to climate change–related increases in heat exposure in elderly people, as well as increases in diarrheal disease, malaria, dengue, coastal flooding, and childhood stunting.” The impacts will fall disproportionately on vulnerable populations, and climate change “could force more than 100 million people into extreme poverty by 2030.”

The article discusses major exposure pathways that link climate hazards to health outcomes like “heat-related illness and death, illnesses caused by poor air quality, undernutrition from reduced food quality and security, and selected vectorborne diseases.” It also notes that “the effects of climate change on mental health are increasingly recognized.”

Importantly, the authors argue that “opportunities exist to capitalize on environmental data to develop early warning and response systems” to help adaptation efforts. Furthermore, “investments in and policies to promote proactive and effective adaptation and reductions in greenhouse-gas emissions (mitigation) would decrease the magnitude and pattern of health risks.”

The article highlights that “transitions in land, energy, industry, buildings, transportation, and cities” aimed at “limiting global warming to 1.5°C” would bring substantial public health benefits. For example, “strong climate policies consistent with the 2°C Paris Agreement target could prevent approximately 175,000 premature deaths” in the US by 2030. More broadly, the authors state that “policies to reduce greenhouse-gas emissions in the energy sector, housing, transportation; and agriculture and food systems can result in near-term ancillary benefits to human health.”

The review thus underscores that “protecting [public] health demands decisive actions from health professionals and governments” in tackling climate change through adaptation and ambitious mitigation policies that yield health “co-benefits.”

What is the role of education?

The review article presents clear evidence that climate change is already severely harming public health, with escalating threats projected, particularly for vulnerable communities. It rightly argues that responding effectively requires urgent adaptation and emissions reductions prioritizing those most impacted.

However, conventional top-down approaches to climate and health in global health are unlikely to achieve the rapid, scalable results needed. Such traditional modalities tend to be ponderously slow, generate knowledge not readily actionable, and fail to reach those on the frontlines in marginalized locales.

Building a new scientific field around climate and health may take years using conventional approaches.

What we would wish for instead is a decentralized, grassroots peer learning system that can directly empower and assist under-resourced local health workers confronting growing climate-health crises.

Specifically, a digital network interconnecting one million such frontline personnel to share granular insights on how climate change is damaging community health in their areas.

This system would facilitate collaborative design of hyperlocal adaptation initiatives tailored to each locale’s distinct climate-health challenges.

It would channel localized knowledge to shape responsive national policies rooted in lived realities on the ground.

Digital tools would amplify voices of those observing firsthand impacts too often excluded.

And participatory methods would synthesize nuanced community observations lacking in conventional statistics.

This locally-attuned, equity-oriented learning infrastructure could unlock community leadership to catalyze climate-health solutions where needs are greatest. 

It represents the kind of decentralized, rapidly scalable approach essential to address the review’s calls for urgent action assisting vulnerable groups most harmed by climate change.

Reference: Haines, A., Ebi, K., 2019. The Imperative for Climate Action to Protect Health. N Engl J Med 380, 263–273. https://doi.org/10.1056/NEJMra1807873

Illustration: The Geneva Learning Foundation Collection © 2024