Stories

Category: Global health

  • Learning about mental health and psychosocial needs in Ukraine and affected countries

    Learning about mental health and psychosocial needs in Ukraine and affected countries

    The report “Two years on: mental health and psychosocial needs in Ukraine and affected countries” is from the Psychosocial Support Centre, a specialized hub of the International Federation of Red Cross and Red Crescent Societies (IFRC) with the mission to “enhance psychosocial support initiatives”.

    Key points from the report include:

    • Nearly “one in ten of those affected by war grapple with moderate to severe mental health issues.” This refers to the crisis having significant psychological impacts on those directly impacted or displaced by the conflict.
    • Over 1 million crisis-affected people have received psychosocial support (PSS) “thanks to specialist staff and more than 124,000 volunteers from 58 countries.” 
    • There are “increased psychological assistance requests…from women heading households” as Ukraine sees heightened risks to families and disruptions to support services due to the conflict. 
    • “Three out of four parents report signs of psychological trauma in their children” including impaired memory, inattention, and learning difficulties. Children are especially vulnerable to the stresses and trauma resulting from the conflict. 
    • Psychological First Aid (PFA) services are provided “at Humanitarian Service Points along refugee routes, through call centers, and at various contact points”.

    Overall, the report highlights the substantial scale and complex nature of MHPSS (mental health and psychosocial support) needs driven by the Ukraine conflict as well as the scale and scope of the Red Cross Red Crescent response mobilized so far including through delivery of PFA (Psychological First Aid) and PSS (psychosocial support).

    What are the challenges?

    The report on mental health and psychosocial needs in Ukraine highlights several key challenges, including:

    • The vast scale of needs driven by protracted conflict, with 14.6 million people requiring humanitarian assistance. Meeting mental health demands for crisis-affected populations often exceeds available capacity and resources.
    • Ensuring consistent, sustainable care and support with constrained funding and risk of donor fatigue as the crisis persists long-term. Services must have resilience even as attacks continue disrupting infrastructure.
    • Reaching vulnerable groups like the elderly and immobile with limited mobility to access care. Specialized outreach and home-based care is essential but demanding to deliver.
    • Preventing burnout, fatigue and declining wellbeing among staff and volunteers working under intense pressure in risky environments. Their mental health and capacity is vital but often overlooked.

    What can we learn about psychological first aid (PFA) for children from this report?

    First, we need to understand the specialized terminology used:

    • The term “MHPSS” (mental health and psychosocial support) refers to a continuum of support aimed at protecting and improving people’s mental health and wellbeing during and after crises. The report notes resourcing this immense and growing scale of MHPSS need remains an acute challenge.
    • Psychological First Aid (“PFA”) describes a humane, supportive response to a fellow human being who is suffering and who may need support.
    • Child Friendly Spaces (CFS) are a key element of the Red Cross Red Crescent psychosocial support response in Ukraine. They are “a service to increase children’s access to safe environments and promote their psychosocial well-being.”

    We learn that with support from the IFRC Psychosocial Centre, the Ukrainian Red Cross Society:

    • has provided recreational activities to almost 70,000 children in CFS inside Ukraine over the past year;
    • trained 319 staff and volunteers in managing CFS;
    • runs CFS to help children cope with issues like difficulties meeting new people, separation anxiety, and fear when air raid sirens sound.

    The report shares anecdotes from children, such as a child who came to a CFS in Kyiv after fleeing heavy shelling. His social anxiety has improved and he asks his mom if he can skip school to go to CFS activities instead.

    More data, supported by analysis on outcomes and effectiveness, could further strengthen the report.

    How can peer learning be useful?

    A peer learning model focused on improving health outcomes is likely to be relevant in addressing these multilayered challenges. It is specifically designed to foster reflection and unlock intrinsic motivation in practitioners to create change.

    • Peer learning methodologies could help meet capacity gaps by scaling support across affected areas rapidly through digital means.
    • Peer support networks could enable volunteers and staff caring for others to also care for themselves, preventing fatigue. 
    • By connecting practitioners across borders and sectors, peer learning could help to share innovative, context-appropriate solutions and accelerate their testing and refinement to meet needs.

    Reference: Two years on: mental health and psychosocial needs in Ukraine and affected countries. Psychosocial Support Centre, Copenhagen, Denmark.

    Image: Psychosocial Support Centre Report cover.

  • Why lack of continuous learning is the Achilles heel of immunization 

    Why lack of continuous learning is the Achilles heel of immunization 

    Continuous learning is lacking in immunization learning culture, a measure of the capacity for change..

    This lack may be an underestimated barrier to the “Big Catch-Up” and reaching zero-dose children

    This was a key finding presented at Gavi’s Zero-Dose Learning Hub (ZDLH) webinar “Equity in Action: Local Strategies for Reaching Zero-Dose Children and Communities” on 24 January 2024.

    The finding is based on analysis large-scale learning culture measurements conducted by the Geneva Learning Foundation in 2020 and 2022, with more than 10,000 immunization staff from all levels of the health system, job categories, and contexts, responding from over 90 countries.

    YearnContinuous learningDialogue & InquiryTeam learningEmbedded SystemsEmpowered PeopleSystem ConnectionStrategic Leadership
    202038303.614.68–4.814.685.104.83
    202261853.764.714.864.934.725.234.93
    TGLF global measurements (2020 and 2022) of learning culture in immunization, using the Dimensions of Learning Organization Questionnaire (DLOQ)

    What does this finding about continuous learning actually mean?

    In immunization, the following gaps in continuous learning are likely to be hindering performance.

    1. Relatively few learning opportunities for immunization staff
    2. Limitations on the ability for staff to experiment and take risks 
    3. Low tolerance for failure when trying something new
    4. A focus on completing immunization tasks rather than developing skills and future capacity
    5. Lack of encouragement for on-the-job learning 

    This gap hurts more than ever when adapting strategies to reach “zero-dose” children.

    These are children who have not been reached when immunization staff carry out what they usually do.

    The traditional learning model is one in which knowledge is codified into lengthy guidelines that are then expected to trickle down from the national team to the local levels, with local staff competencies focused on following instructions, not learning, experimenting, or preparing for the future.

    For many immunization staff, this is the reference model that has helped eradicate polio, for example, and to achieve impressive gains that have saved millions of children’s lives.

    It can therefore be difficult to understand why closing persistent equity gaps and getting life-saving vaccines to every child would now require transforming this model.

    Yet, there is growing evidence that peer learning and experience sharing between health workers does help surface creative, context-specific solutions tailored to the barriers faced by under-immunized communities. 

    Such learning can be embedded into work, unlike formal training that requires staff to stop work (reducing performance to zero) in order to learn.

    Yet the predominant culture does little to motivate or empower these workers to recognize or reward such work-based learning.

    Furthermore, without opportunities to develop skills, try new approaches, and learn from both successes and failures, staff may become demotivated and ineffective. 

    This is not an argument to invest in formal training.

    Investment in formal training has failed to measurably translate into improved immunization performance.

    Worse, the per diem economy of extrinsic incentives for formal training has, in some places, led to absurdity: some health workers may earn more by sitting in classrooms than from doing their work.

    With a weak culture of learning, the system likely misses out on practices that make a difference.

    This is the “how” that bridges the gap between best practice and what it takes to apply it in a specific context.

    The same evidence also demonstrates a consistently-strong correlation between strengthened continuous learning and performance.

    Investment in continuous learning is simple, costs surprisingly little given its scalability and effectiveness.

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

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

    That means investment in continuous learning is already proven to result in improved performance.

    We call this “learning-based work”.

    References

    Watkins, K.E. and Marsick, V.J., 2023. Chapter 4. Learning informally at work: Reframing learning and development. In Rethinking Workplace Learning and Development. Edward Elgar Publishing. Excerpt: https://stories.learning.foundation/2023/11/04/how-we-reframed-learning-and-development-learning-based-complex-work/

    The Geneva Learning Foundation. From exchange to action: Summary report of Gavi Zero-Dose Learning Hub inter-country exchanges. Geneva: The Geneva Learning Foundation, 2023. https://doi.org/10.5281/zenodo.10132961

    The Geneva Learning Foundation. Motivation, Learning Culture and Immunization Programme Performance: Practitioner Perspectives (IA2030 Case Study 7) (1.0); Geneva: The Geneva Learning Foundation, 2022. https://doi.org/10.5281/zenodo.7004304

    Image: The Geneva Learning Foundation Collection © 2024

  • What is the relationship between leadership and performance?

    What is the relationship between leadership and performance?

    In their article “What Have We Learned That Is Critical in Understanding Leadership Perceptions and Leader-Performance Relations?”, Robert G. Lord and Jessica E. Dinh review research on leadership perceptions and performance, and provide research-based principles that can provide new directions for future leadership theory and research.

    What is leadership? 

    Leadership is tricky to define. The authors state: “Leadership is an art that has significant impact on individuals, groups, organizations, and societies”.

    It is not just about one person telling everyone else what to do. Leadership happens in the connections between people – it is something that grows between a leader and followers, almost like a partnership. And it usually does not involve just one leader either. There can be leadership shared across a whole team or organization.

    The big question is: how does all this connecting and partnering actually get a team to perform well? That is what researchers are still trying to understand.

    What we do know about leadership

    Researchers have learned a lot about what makes a leader “seem” effective to the people around them. Certain personality traits, behaviors, speaking styles and even body language can make people think “oh, that person is a good leader.” 

    But figuring out how those leaders actually influence performance over months and years is tougher. It is hard for scientists to measure stuff that happens slowly over time. More research is still needed to connect the dots between leaders’ actions today and results years later.

    How people think about leadership matters 

    Learning science shows that how people process information shapes their perceptions, emotions and behaviors. So to understand leadership, researchers are now looking into things like:

    • How do the automatic, gut-level parts of people’s brains affect leadership moments? (This means how emotions and instincts influence leadership)
    • How do leaders’ and followers’ thinking interact?  
    • How do emotions and body language play a role?

    This research might help explain why leadership works or does not work in real teams.  

    Some pitfalls to avoid 

    There are a few assumptions that could mislead leadership research:   

    1. Surveys might not catch real leadership behavior, because people’s memories are messy. Their responses involve lots of other stuff beyond just the facts.  
    2. What worked well for leaders in the past might not keep working in a fast-changing world. They cannot just keep doing the same thing.
    3. Leaders actually have less control than we think. Their organization’s success depends on unpredictable factors way beyond what they do.

    The future of leadership research has to focus more on the complex thinking and system-wide stuff that is hard to see but really important. The human brain and human groups are just too complicated for simple explanations.

    Reference: Lord, R.G., Dinh, J.E., 2014. What Have We Learned That Is Critical in Understanding Leadership Perceptions and Leader-Performance Relations? Industrial and Organizational Psychology 7, 158–177.

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

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

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

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

    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:

    $latex \text{Total Number of Coached Learners} = M \times K&s=3$

    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:

    $latex \text{Total Number of Learning Interactions} = \frac{N \times (N – 1)}{2}&s=3$

    $latex \text{The number of learning interactions scales with: } O(N^2)&s=3$

    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:

    $latex \text{Total Number of Learning Interactions} = N \times 3&s=3$

    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:

    $latex O(N)&s=3$

    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

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

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

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

    $latex \text{Efficacy} = \frac{S \cdot w_S + I \cdot w_I + C \cdot w_C + F \cdot w_F + U \cdot w_U}{w_S + w_I + w_C + w_F + w_U}&s=3$

    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
    $latex w_S&s=3$$latex w_I&s=3$$latex w_C&s=3$$latex w_F&s=3$$latex w_U&s=3$
    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).

    $latex \text{ESS} = E \times N&s=3$

    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, $latex (E_{\text {peer}})&s=2$ , 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, $latex (E_{\text {cascade}})&s=2$, 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, $latex (E_{\text {expert}})&s=2$, 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?

    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:

    $latex K_n = K \cdot \alpha^n&s=3$

    • Where $latex K_n$ is the knowledge at the nth level of the cascade. As n grows, $latex K_n$ 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

  • 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

    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

    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: strengthening health workforce through new learning models

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

    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