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Tag: health workers

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

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

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

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

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

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

    I want to explore three questions:

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

    Artificial intelligence within punitive accountability structures of global health

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

    Should we interpret this as disengagement from peer learning?

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

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

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

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

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

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

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

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

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

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

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

    The economics of knowledge in global health contexts

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

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

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

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

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

    A new AI divide

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

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

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

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

    Confronting power dynamics in AI integration

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

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

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

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

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

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

    Illustration: The Geneva Learning Foundation Collection © 2025

    References

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

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

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

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

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

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

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

  • Why answer Teach to Reach Questions?

    Why answer Teach to Reach Questions?

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

    What are Teach to Reach Questions?

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

    How does it work?

    1. You choose what to share: Answer only questions where you have actual experience. No need to respond to everything – focus on what matters to you.
    2. Share specific moments: Instead of general information, we ask about real situations you’ve faced. What exactly happened? What did you do? How did you know it worked?
    3. Learn from others: Within weeks, you’ll receive a collection of experiences shared by health workers from over 70 countries. See how others solved problems similar to yours.

    What’s different about these questions?

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

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

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

    What’s in it for you?

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

    1. Get recognized: You’ll be honored as a Teach to Reach Contributor and receive certification.
    2. Learn practical solutions: See how other health workers tackle challenges like yours.
    3. Make connections: At Teach to Reach, you’ll meet others who have been sharing and learning about the same issues.
    4. Access support: Global partners will share how they can support solutions you and other health workers develop.

    A health worker’s experience

    Here is what on community health worker from Kenya said:

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

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

    Ready to start?

    1. Request your invitation to Teach to Reach now.
    2. Look for questions in your inbox.
    3. Share your experience on topics you know about.
    4. Receive the complete collection of shared experiences.
    5. Join us in December to meet others face-to-face.

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

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

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

    Listen to the Teach to Reach podcast:

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

    Image: The Geneva Learning Foundation Collection © 2024

  • Health at COP29: Workforce crisis meets climate crisis

    Health at COP29: Workforce crisis meets climate crisis

    Health workers are already being transformed by climate change. COP29 stakeholders can either support this transformation to strengthen health systems, or risk watching the health workforce collapse under mounting pressures.

    The World Health Organization’s “COP29 Special Report on Climate Change and Health: Health is the Argument for Climate Action“ highlights the health sector’s role in climate action.

    Health professionals are eyewitnesses and first responders to climate impacts on people and communities firsthand – from escalating respiratory diseases to spreading infections and increasing humanitarian disasters.

    The report positions health workers as “trusted members of society” who are “uniquely positioned” to champion climate action.

    The context is stark: WHO projects a global shortage of 10 million health workers by 2030, with six million in climate-vulnerable sub-Saharan Africa. Meanwhile, our communities and healthcare systems already bear the costs of climate change through increasing disease burdens and system strain.

    Health workers are responding, because they have to. Their daily engagement with climate-affected communities offers insights that can strengthen both health systems and climate response – if we learn to listen.

    A “fit-for-purpose” workforce requires rethinking learning and leadership

    WHO’s report acknowledges that “scale-up and increased investments are necessary to build a well-distributed, fit-for-purpose workforce that can meet accelerating needs, especially in already vulnerable settings.” The report emphasizes that “governments and partners must prioritize access to decent jobs, resources, and support to deliver high-quality, climate-resilient health services.” This includes ensuring “essential protective equipment, supplies, fair compensation, and safe working conditions such as adequate personnel numbers, skills mix, and supervisory capacity.”

    Resources, skills, and supervision are building blocks of every health system.

    They are necessary but likely to be insufficient.

    Such investments could be maximized through cost-effective, scalable peer learning networks that enable rapid knowledge sharing and solution development – as well as their locally-led implementation.

    The WHO report calls for “community-led initiatives that harness local knowledge and practices.”

    Our analyses – formed by listening to and learning from thousands of health professionals participating in the Teach to Reach peer learning platform – suggest that the expertise developed by health professionals through daily engagement with communities facing climate impacts is key to problem-solving, to implementing local solutions, and to ensure that communities are part and parcel of such solutions.

    Why move beyond seeing health workers as implementers of policies or recipients of training?

    We stand to gain much more if their leadership is recognized, nurtured, and supported.

    This is a notion of leadership that diverges from convention: if health workers have leadership potential, it is because they are uniquely positioned to turn what they know – because they are there every day – into action.

    Peer learning has the potential to significantly accelerate progress toward country and global goals for climate change and health.

    By making connections, a health professional expands the horizon of what they are able to know.

    At the Geneva Learning Foundation, we have seen that such leadership emerges when health workers are empowered to:

    • share and validate their experiential knowledge;
    • develop, test, and implement solutions with the communities they serve, using local resources;
    • connect with peers facing similar challenges; and
    • inform policy based on ground-level realities.

    Working with a global community of community-based health workers, we co-developed the Teach to Reach platform, community, and network to listen and learn at scale. Unlike traditional training programs, Teach to Reach creates a peer learning ecosystem where:

    • Health workers from over 70 countries connect directly to share experiences.
    • Solutions are crowdsourced from those closest to the challenges.
    • Knowledge flows horizontally rather than just vertically.
    • Local innovations are rapidly shared and adapted across contexts.

    For example, in June 2024, over 21,000 health professionals participated in Teach to Reach 10, generating hundreds of real-world stories and insights about climate change impacts on health.

    The platform has proven particularly valuable in fragile contexts and resource-limited settings, where traditional capacity building approaches often struggle to reach or engage health workers effectively.

    This approach does not replace formal institutions or traditional scientific methods – instead, it creates new pathways for knowledge to flow rapidly between communities, while building the collective capacity needed to respond to accelerating climate impacts on health.

    Already, this demonstrates the untapped potential for health workers to contribute to our collective understanding and response.

    But we do not stop there.

    As we count down to Teach to Reach 11, participants are now sharing how they have actually used and applied this peer knowledge to make progress against their local challenges.

    They cannot do it alone.

    This is why we ask global partners to join and contribute to this emergent, locally-led leadership for change.

    How different is this ‘ask’ from that of global partners asking health workers to contribute to the climate change and health agenda?

    WHO’s COP29 report makes a powerful case that “community-led initiatives that harness local knowledge and practices in both climate action and health strategies are fundamental for creating interventions that are both culturally appropriate and effective.”

    Furthermore, it recognizes that “these initiatives ensure that climate and health solutions are tailored to the specific needs and realities of those most impacted by climate change but also grounded in their lived realities.”

    What framework for collaboration?

    The path forward requires what the report describes as “cooperation across sectors, stakeholders and rights-holders – governmental institutions, local authorities, local leaders including religious authorities and traditional medicine practitioners, NGOs, businesses, the health community, Indigenous Peoples as well as local communities.”

    Our experience with Teach to Reach demonstrates how such cooperation can be facilitated at scale through digital platforms that enable peer learning and knowledge sharing. Key elements include:

    • a structured yet flexible framework for sharing experiences and insights;
    • direct connections between health workers at all levels of the system;
    • rapid feedback loops between local implementation and broader learning;
    • support for health workers to document and share their innovations; and
    • mechanisms to validate and spread effective local solutions.

    WHO’s recognition that health workers have “a moral, professional and public responsibility to protect and promote health, which includes advocating for climate action, leveraging prevention for climate mitigation and cost savings, and safeguarding healthy environments” sets a clear mandate.

    This WHO report highlights the need for new ways of supporting community-led learning and action to:

    1. support the rapid sharing of local solutions;
    2. build health worker capacity through peer learning;
    3. connect communities facing similar challenges; and
    4. enable health workers to lead change in their communities

    Reference

    Neira, M. et al. (2024) COP 29 Special Report on Climate Change and Health: Health is the Argument for Climate Action. Geneva, Switzerland: World Health Organization.

    Image: The Geneva Learning Foundation Collection © 2024

  • Integrating community-based monitoring (CBM) into a comprehensive learning-to-action model

    Integrating community-based monitoring (CBM) into a comprehensive learning-to-action model

    According to Gavi, “community-based monitoring” or “CBM” is a process where service users collect data on various aspects of health service provision to monitor program implementation, identify gaps, and collaboratively develop solutions with providers.

    • Community-based monitoring (CBM) has emerged as a promising strategy for enhancing immunization program performance and equity.
    • CBM interventions have been implemented across different settings and populations, including remote rural areas, urban poor, fragile/conflict-affected regions, and marginalized groups such as indigenous populations and people living with HIV.

    By engaging service users, CBM aims to foster greater accountability and responsiveness to local needs.

    • However, realizing CBM’s potential in practice has proven challenging.
    • Without a coherent approach, CBM risks becoming just another disconnected tool.

    The Geneva Learning Foundation’s innovative learning-to-action model offers a compelling framework within which CBM could be applied to immunization challenges.

    The model’s comprehensive design creates an enabling environment for effectively integrating diverse monitoring data sources – and this could include community perspectives.

    Health workers as trusted community advisers… and members of the community

    A distinctive feature of TGLF’s model is its emphasis on health workers’ role as trusted advisors to the communities they serve.

    The model recognizes that local health staff are not merely service providers, but often deeply embedded community members with intimate knowledge of local realities.

    For example, in TGLF’s immunization learning initiatives, participating health workers frequently share insights into the social, cultural, and economic factors shaping vaccine hesitancy and uptake in their communities.

    • They discuss the everyday barriers families face, from misinformation to transportation challenges, and strategize context-specific outreach approaches.
    • This grounding in community realities positions health workers as vital bridges for facilitating community engagement in monitoring.

    When local staff are empowered as active agents of learning and change, they can more effectively champion community participation, translating insights into tangible improvements.

    Could CBM fit into a more comprehensive system from local monitoring to action?

    TGLF’s model supports health workers in this bridging role by providing a comprehensive framework for local monitoring and action.

    Through peer learning networks and problem-solving cycles, the model equips health staff to collect, interpret, and act on unconventional monitoring data from their communities.

    For instance, in TGLF’s 2022 “Full Learning Cycle” initiative, 6,185 local health workers from 99 countries examined key immunization indicators to inform their analyses of root causes and then map out corrective actions.

    • Participants began monitoring their own local health indicators, such as vaccination coverage rates.
    • For many, this was the first time they had been prompted to use this data for problem-solving a real-world challenge they face, rather than just reporting up the next level of the health system.

    They discussed many factors critical for tailoring immunization strategies.

    This transition – from being passive data collectors to active data users – has proven transformative.

    It positions health workers not as cogs in a reporting machine, but as empowered analysts and strategists.

    By discussing real metrics with peers, participants make data actionable and contextually meaningful.

    Guided by expert-designed rubrics and facilitated discussions, health workers translated this localized monitoring data into practical improvement plans.

    For an epidemiologist, this represents a significant shift from traditional top-down monitoring paradigms.

    By valuing and actioning local knowledge, TGLF’s model demonstrates how community insights can be systematically integrated into immunization decision-making.

    However, until now, its actors have been health workers, many of them members of the communities they serve, not service users themselves.

    CBM’s focus on monitoring is important – but leaves out key issues around community participation, decision-making autonomy, and strategy.

    How could we integrate CBM into a transformative approach?

    TGLF’s experiences suggest that CBM could be embedded within comprehensive learning-to-action systems focused on locally-led change.

    TGLF’s model is more than a monitoring intervention.

    • It combines structured learning, rapid solution sharing, root cause analysis, action planning, and peer accountability to drive measurable improvements.
    • These mutually reinforcing components create an enabling environment for health workers to translate insights into impact.

    In this framing, community monitoring becomes one critical input within a continuous, collaborative process of problem-solving and adaptation.

    Several features of TGLF’s model illustrate how this integration could work in practice:

    1. Peer accountability structures, where health workers regularly convene to review progress, share challenges, and iterate solutions, create natural entry points for discussing and actioning community feedback.
    2. Rapid dissemination channels, like TGLF’s “Ideas Engine” for spreading promising practices across contexts, enable local innovations in response to community-identified gaps to be efficiently scaled.
    3. Emphasis on root cause analysis and systemic thinking equips health workers to interpret community insights within a broader ecosystem lens, connecting localized issues to upstream determinants.
    4. Cultivation of connected leadership empowers local actors to champion community priorities and navigate complex change processes.

    TGLF’s extensive digital network connects health workers across system levels and contexts, enabling them to learn from each other’s experiences with no upper limit to the number of participants.

    By contrast, CBM seems to assume that a community is limited to a physical area, which fails to recognize that problem-solving complex challenges requires expanding the range of inputs used.

    Within a networked approach that connects both community members and health workers across boundaries of geography, health system level, and roles, CBM could become an integral component of a transformative approach to health system improvement – one that recognizes communities and local health workers as capable architects of context-responsive solutions.

    Fundamentally, the TGLF model invites a shift in mindset about whose expertise counts in monitoring and driving system change.

    CBM could provide the ‘connective tissue’ for health workers to revise how they listen and learn with the communities they serve.

    For immunization programs grappling with persistent inequities, this shift from passive compliance to proactive local problem-solving is critical.

    As the COVID-19 crisis has underscored, rapidly evolving public health challenges demand localized action that harnesses the full range of community expertise.

    TGLF’s model offers a tested framework for actualizing this vision at scale.

    By investing in local health workers’ capacity to learn, adapt, and lead change in partnership with the communities they serve, the model illuminates a promising pathway for integrating CBM into immunization monitoring and beyond.

    For epidemiologists and global health practitioners, TGLF’s approach invites a reframing of how we conceptualize and operationalize community engagement in health system monitoring.

    It challenges us to move beyond tokenistic participation towards genuine co-design and co-ownership of monitoring processes with local actors.

    Realizing this vision will require significant shifts in mindsets, power dynamics, and resource flows.

    But as TGLF’s initiatives demonstrate, when we invest in the leadership of those closest to the challenges we seek to solve, transformative possibilities emerge.

    Further rigorous research comparing the impacts of different CBM integration models could help accelerate this paradigm shift, surfacing critical lessons for the immunization field and global health more broadly.

    TGLF’s model not only offers compelling lessons for reimagining monitoring and improvement in immunization programs, it also provides a pathway for integrating CBM into a system that supports actual change.

    CBM practitioners are likely to struggle with how to incorporate it into existing practices.

    By investing in frontline health workers as change agents, and surrounding them with an empowering learning ecosystem, the model offers a path to then bring in community monitoring.

    Without such leadership from health workers, it is unlikely that communities are able to participate.

    The journey to authentic community engagement in health system monitoring is undoubtedly complex.

    But if we are to deliver on the promise of equitable immunization for all, it is a journey we must undertake.

    TGLF’s model lights one promising path forward – one that positions communities and local health workers as the beating heart of a learning health system.

    While Gavi’s evidence brief affirms the promise of CBM for immunization, TGLF’s experience with its own model suggests the full potential of CBM may be realized by embedding it within more comprehensive, digitally-enabled learning systems that activate health workers as agents of change – and do so with both physical and digital communities implementing new forms of peer and community accountability that complement conventional kinds (supervision, administration, donor, etc.).

  • Women’s voices from the frontlines of health and humanitarian action

    Women’s voices from the frontlines of health and humanitarian action

    English version | Version française

    GENEVA, Switzerland, 8 March 2024 – The Geneva Learning Foundation (TGLF) is sharing a collection of stories titled “Women inspiring women”, shared by 177 women on the frontlines of health and humanitarian action.

    Download: The Geneva Learning Foundation. (2024). Women inspiring women: International Women’s Day 2024 (1.0). https://doi.org/10.5281/zenodo.10783218

    The collection is a vibrant tapestry of women’s voices from the frontlines of health and humanitarian action, woven together to showcase the resilience, passion, and leadership of women who are making a difference in the face of war, disease, and climate change.

    TGLF reached out to women in its global network of more than 60,000 health workers, inviting them to share their heartfelt advice and vision for the future with young women and girls.

    Health workers in this network, men and women, are on the frontlines of adversity: they work in remote rural areas or with the urban poor. Many support the needs of nomadic and migrant populations, refugees, and internally-displaced populations (IDPs). 

    Imagine being able to sit down with a community health worker in Nigeria, a nurse in India, or a doctor in Brazil, and listen to their stories of triumph and struggle. “Women Inspiring Women” makes that possible, bringing together voices that are rarely heard on the global stage.

    The responses are raw, honest, and deeply moving.

    From remote villages to urban slums, women work to build a better future for their communities.

    What makes this collection truly unique is its authenticity and diversity. 

    “In a world of war, disease, and a worsening climate, literacy is vital for the next generation of women and girls to make better choices concerning health, marriage, and income. Literacy is key in transforming households out of poverty, no matter who they are or where they are born.” – Hauwa Abbas, Public health specialist (MPH), Nigeria

    Through their words, these women offer invaluable guidance to the next generation of female leaders. They share the lessons they’ve learned, the challenges they’ve faced, and the hopes they hold for a world where every girl can live a healthy, fulfilling life, no matter where she is born.

    “Serving humanity as a health or humanitarian worker is one of the most rewarding careers one can engage in. Though it requires a lot of hard work more importantly and what is usually not thought about is the heart work it involves. The ability to empathize with the sick and those in humanitarian needs is a key ingredient for success.” – Ngozi Kennedy MB ChB, MPH, Public health specialist, Ethiopia

    “This collection is a celebration of the incredible resilience and leadership of women health workers and humanitarians worldwide,” said TGLF Executive Director Reda Sadki. “It’s a testament to the power of storytelling to inspire change and unite us in our shared vision for a better future.”

    “Insist on making generational impact as a woman against ALL odds! Don’t give up, don’t give in, don’t give way! Persistence wears out resistance! This is my success story today as I battled many challenges to establish rotavirus surveillance in my country as well as rotavirus vaccine introduction advocacy which has finally culminated in the vaccine introduction in Nigeria.” – Professor Beckie Tagbo, Doctor, Institute of Child Health, University of Nigeria Teaching Hospital, Enugu, Nigeria

    In the lead up to International Women’s Day, TGLF has been sharing sneak peeks of the stories and quotes on its social media platforms. Follow along on LinkedIn, Twitter/X, Facebook, Instagram and Telegram to get a glimpse of the inspiration that awaits.

    “Women Inspiring Women” is more than just a collection of stories. It’s a rallying cry for gender equality, a celebration of women’s leadership, and a reminder of the incredible impact one voice can have. Get ready to be inspired, moved, and empowered by the voices of women health workers and humanitarians worldwide.

    Join us in amplifying the voices of these extraordinary women and creating a world where every girl can thrive.

    “Resilience and determination in the face of difficulties will be essential – it is vital not to be deterred or discouraged when faced with setbacks of adversity, which are an inevitability in these spheres. Health or humanitarian work is all about people. There may be days where you question your decision and that is where determination keeps you going.” – Genise Pascal-Ferrer Iglesias, Coordinator of Imaging Services, Goodwill, Dominica

    “Empowered women empower women. Ever since you were born, I kept you with me in all my philanthropic activities. […] I wish you all the blessings, happiness and success in life. Someday, you will write a similar letter to your own daughter saying, ‘Empowered women empower women’.” – Dr Faiza Rabbani, Public health specialist (MPH), Lahore District, Punjab Province, Pakistan

    Download “Women inspiring women” via this link https://doi.org/10.5281/zenodo.10783218

    About the Geneva Learning Foundation

    Learn more about The Geneva Learning Foundation: https://doi.org/10.5281/zenodo.7316466

    Created by a group of learning innovators and scientists with the mission to discover new ways to lead change, TGLF’s team combines over 70 years of experience with both country-based (field) work and country, region, and global partners.

    • Our small, fully remote agile team already supports over 60,000 health practitioners leading change in 137 countries.
    • We reach the front lines: 21% face armed conflict; 25% work with refugees or internally-displaced populations; 62% work in remote rural areas; 47% with the urban poor; 36% support the needs of nomadic/migrant populations.

    TGLF’s unique package:

    1. Helps local actors take action with communities to tackle local challenges, and
    2. provides the tools to build a global network, platform, and community of health workers that can scale up local impact for global health.

    In 2019, research showed that TGLF’s approach can accelerate locally-led implementation of innovative strategies by 7X, and works especially well in fragile contexts.

    Photo: The Geneva Learning Foundation Collection © 2024

  • Before, during, and after COP28: Climate crisis and health, through the eyes of health workers from Africa, Asia, and Latin America 

    Before, during, and after COP28: Climate crisis and health, through the eyes of health workers from Africa, Asia, and Latin America 

    Samuel Chukwuemeka Obasi, a health professional from Nigeria:

    “Going back home to the community where I grew up as a child, I was shocked to see that most of the rivers we used to swim and fish in have all dried up, and those that are still there have become very shallow so that you can easily walk through a river you required a boat to cross in years past.”

    In July 2023, more than 1200 health workers from 68 countries shared their experiences of changes in climate and health, at a unique event designed to shed light on the realities of climate impacts on the health of the communities they serve.

    Before, during and after COP28, we are sharing health workers’ observations and insights.

    Follow The Geneva Learning Foundation to learn how climate change is affecting health in multiple ways:

    • How extreme weather events can lead to tragic loss of life.
    • How changing weather patterns are leading to crop failures and malnutrition, and forcing people to abandon their homes.
    • How infectious diseases are surging as mosquitoes proliferate and water sources are contaminated.
    • How climate stresses are particularly problematic for those with existing health conditions, like cardiovascular disease and diabetes.
    • How climate impacts are having a devastating effect on mental health as people’s ways of life are destroyed.
    • How climate change is changing the very fabric of society, driving displacement and social hardship that undermines health and wellbeing.
    • How a volatile climate is disrupting the delivery of essential health services and people’s ability to access them.
    • We will finish the series with  inspiring stories of how health workers are already responding to such challenges, working with communities to counter the effects of a changing climate.

    On 1 December 2023, TGLF will be publishing a compendium and analysis of these 1200 contributions – On the frontline of climate change and health: A health worker eyewitness report. Get the report…

    This landmark report – a global first – kickstarts our campaign to ensure that health workers in the Global South are recognized as:

    • The people already having to manage the impacts of climate change on health.
    • An essential voice to listen to in order to understand climate impacts on health.
    • A potentially critical group to work with to protect the health of communities in the face of a changing climate.

    Before, during, and after COP28, we are advocating for the recognition and support of health workers as trusted advisers to communities bearing the brunt of climate change effects on health.

    Watch the Special Event: From community to planet: Health professionals on the frontlines of climate change