Prince Andrew has been excised from the royal family. Should the monarchy do more?
Anyone else wondering why the royal family has not just catapulted Prince Andrew to a deserted island, or…
#NewsBeep #News #Headlines #AU #Australia #DukeofYork #emilymaitlis #epsteinfiles #hrh #investigation #jeffreyepstein #juliabaird #KingCharles #memoir #princeandrew #princeandrewtitles #SarahFerguson #VirginiaGiuffre
https://www.newsbeep.com/209088/
Prince Andrew, Harry, Meghan and King Edward VIII show what losing a title says about the House of Windsor
Prince Andrew finally fell on his sword on the weekend and announced he would give up using his…
#NewsBeep #News #Headlines #AU #Australia #churchill #dukeofsussex #DukeofYork #hrh #jeffreyepstein #kindcharles #meghan #princeandrew #PrinceHarry #PrinceWilliam #QueenElizabeth #royalfamily #SarahFerguson #scandal #titles #winston
https://www.newsbeep.com/198204/
Prince Harry and Meghan Markle asked to give up their royal titles
https://newstainmentora.blogspot.com/2025/05/prince-harry-and-meghan-markle-asked-to.html
#PrinceHarry #MeghanMarkle #RoyalFamily #hrh #hrhduchessofsussex #hrhduchessmeghan #royaltitles
Prince Harry and Meghan Markle still rely on royal ties
https://newstainmentora.blogspot.com/2025/05/prince-harry-and-meghan-markle-still.html
#PrinceHarry #MeghanMarkIe #RoyalFamily #hrh #hrhtitles #hrhduchessmeghan #hrhduchessofsussex
Insider selects royal to strip Harry and Meghan's royal status that is 'not worth it'
https://newstainmentora.blogspot.com/2025/05/insider-selects-royal-to-strip-harry.html
#princeharry #MeghanMarkle #RoyalFamily #hrh #hrhtitles #hrhduchessmeghan #duchessofsussex
Meghan Markle and Prince Harry are labeled 'hypocrites' for their attitude
https://newstainmentora.blogspot.com/2025/05/meghan-markle-and-prince-harry-are_3.html
#MeghanMarkle #PrinceHarry #RoyalFamily #hrh #hrhduchessmeghan
Prince Harry and Meghan Markle 'not shocked' by William's decision to remove their titles
https://newstainmentora.blogspot.com/2025/05/prince-harry-and-meghan-markle-not.html
#PrinceHarry #MeghanMarkle #princewilliam #hrh #hrhduchessmeghan #duchessofsussex #hrhtitle #RoyalFamily
Meghan Markle is mocked for sticking to ancient titles by 'destroyers'
https://newstainmentora.blogspot.com/2025/05/meghan-markle-ismocked-for-sticking-to.html
#MeghanMarkle #hrhduchessmeghan #hrh #hrhtitle #duchessmeghan #duchessofsussex
Meghan Markle's 'egocentric affectation' toward her HRH title takes an ugly turn
https://newstainmentora.blogspot.com/2025/05/meghan-markles-egocentric-affectation.html
#MeghanMarkle #duchessofsussex #hrh #hrhtitle #RoyalFamily #duchessmeghan
Meghan Markle accused of utilizing her HRH title to get privileged access
https://newstainmentora.blogspot.com/2025/05/meghan-markleaccused-of-utilizing-her.html
#MeghanMarkle #duchessofsussex #RoyalFamily #hrh #hrhtitle
Meghan Markle addressed Netflix executives using the HRH title?
https://newstainmentora.blogspot.com/2025/05/meghan-markle-addressed-netflix.html
#MeghanMarkle #Netflix #duchessofsussex #hrhtitle #hrh
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:
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
#accountability #accountabilityOverloads #ArtificialIntelligence #compliance #conservatism #globalHealth #healthWorkers #HRH #incentives #innovation #learningCulture #performanceMonitoring #TeachToReach
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:
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:
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:
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:
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
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Discussions at the World Health Summit in Berlin this week have rightly emphasized the role of health workers, especially those directly serving local communities.
Health workers stand at the intersection of climate change and community health.
They are first-hand eyewitnesses and the first line of defense against the impacts of climate on health.
There is real horror in the climate impacts on health they describe.
Read the Health Worker Eyewitness reports “Climate change and health: Health workers on climate, community, and the urgent need for action“ and “On the frontline of climate change and health: A health worker eyewitness report”.
There is also real hope in the local solutions and strategies they are already implementing to help communities survive such impacts, most often without support from their government or from the global community.
There is no alternative to the health workforce as the ones most likely to drive effective adaptation strategies and build trust when it comes to climate change and health.
Their unique value stems from several key factors:
As Dr. Maria Neira from the World Health Organization emphasized at Teach to Reach 10 in June 2024: “We need to use our voice, the power of the voice of health, to convince governments to do three things. First, accelerate the transition to clean sources of energy to stop this disaster. Second, to accelerate the transition to sustainable food systems. And third, to accelerate the transition to better planning of urban areas…” Learn more about Teach to Reach.
https://www.youtube.com/watch?v=ai5RlHRt70A
However, current global health investments often overlook the potential of health workers.
Furthermore, there is a tendency to see them as instruments to implement national plans and policies and recipients for knowledge about climate change that they are assumed to be lacking.
This fails to recognize the potential of health workers to lead, not just execute plans, in the face of climate change impacts on health.
It also fails to recognize the significance and value of local knowledge and experience that health workers hold because they are there every day.
A shift in focus could make health workers the most obvious “best buy” for governments and international funders.
By investing in health workers as agents of change, we can leverage an existing, trusted workforce to rapidly scale up adaptation efforts and rebuild trust in global health initiatives.
One innovative model developed by The Geneva Learning Foundation has shown promise in this area, connecting over 60,000 health practitioners across 137 countries and reaching frontline government staff working for health in conflict zones and other challenging contexts.
This approach not only maximizes the impact of climate-health investments but also strengthens health systems overall, creating a win-win scenario for global health and climate resilience.
Image: The Geneva Learning Foundation Collection © 2024
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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.
By engaging service users, CBM aims to foster greater accountability and responsiveness to local needs.
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.
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.
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.
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:
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.).
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The global health community has long grappled with the challenge of providing effective, scalable training to health workers, particularly in resource-constrained settings.
In recent years, digital learning platforms have emerged as a potential solution, promising to deliver accessible, engaging, and impactful training at scale.
Imagine a digital platform intended to train health workers at scale.
Their theory of change rests on a few key assumptions:
On the surface, this seems sensible.
Mobile optimization recognizes health workers’ technological realities.
Multimedia content seems more engaging than pure text.
Assessments appear to verify learning.
Incentives promise to drive uptake.
Scale feels synonymous with success.
While well-intentioned, such a platform risks falling into the trap of a behaviorist learning agenda.
This is an approach that, despite its prevalence, is a pedagogical dead-end with limited potential for driving meaningful, sustained improvements in health worker performance and health outcomes.
It is a paradigm that views learners as passive recipients of information, where exposure equals knowledge acquisition.
It is a model that privileges standardization over personalization, content consumption over knowledge creation, and extrinsic rewards over intrinsic motivation.
It fails to account for the rich diversity of prior experiences, contexts, and challenges that health workers bring to their learning.
Most critically, it neglects the higher-order skills – the critical thinking, the adaptive expertise, the self-directed learning capacity – that are most predictive of real-world performance.
Clicking through screens of information about neonatal care, for example, is not the same as developing the situational judgment to adapt guidelines to a complex clinical scenario, nor the reflective practice to continuously improve.
Moreover, the metrics typically prioritized by behaviorist platforms – user registrations, course completions, assessment scores – are often vanity metrics.
They create an illusion of progress while obscuring the metrics that truly matter: behavior change, performance improvement, and health outcomes.
A health worker may complete a generic course on neonatal care, for example, but this does not necessarily translate into the situational judgment to adapt guidelines to complex clinical scenarios, nor the reflective practice to continuously improve.
The behaviorist paradigm’s emphasis on information transmission and standardized content may stem from an implicit assumption that health workers at the community level do not require higher-order critical thinking skills – that they simply need a predetermined set of knowledge and procedures.
This view is not only paternalistic and insulting, but it is also fundamentally misguided.
A robust body of scientific evidence on learning culture and performance demonstrates that the most effective organizations are those that foster continuous learning, critical reflection, and adaptive problem-solving at all levels.
Health workers at the frontlines face complex, unpredictable challenges that demand situational judgment, creative thinking, and the ability to learn from experience.
Failing to cultivate these capacities not only underestimates the potential of these health workers, but it also constrains the performance and resilience of health systems as a whole.
Even if such a platform achieves its growth targets, it is unlikely to realize its impact goals.
Health workers may dutifully click through courses, but genuine transformative learning remains elusive.
The alternative lies in a learning agenda grounded in advances of the last three decades learning science.
These advances remain largely unknown or ignored in global health.
This approach positions health workers as active, knowledgeable agents, rich in experience and expertise.
It designs learning experiences not merely to transmit information, but to foster critical reflection, dialogue, and problem-solving.
It replaces generic content with authentic, context-specific challenges, and isolated study with collaborative sense-making in peer networks.
It recognizes intrinsic motivation – the desire to grow, to serve, to make a difference – as the most potent driver of learning.
Here, success is measured not in superficial metrics, but in meaningful outcomes: capacity to lead change in facilities and communities that leads to tangible improvements in the quality of care.
Global health leaders faces a choice: to settle for the illusion of progress, or to invest in the deep, difficult work of authentic learning and systemic change, commensurate with the complexity and urgency of the task at hand.
Image: The Geneva Learning Foundation Collection © 2024
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As world leaders gathered for the COP28 climate conference, the Geneva Learning Foundation called for the insights of health workers on the frontlines of climate and health to be heard amidst the global dialogue.
Ahead of Teach to Reach 10, a new eyewitness report analyses 219 new insights shared by 122 health professionals – primarily those working in local communities across Africa, Asia and Latin America – to two critical questions: How is climate change affecting the health of the communities you serve right now? And what actions must world leaders take to help you protect the people in your care?
(Teach to Reach is a regular peer learning event. The tenth edition on 20-21 June 2024 is expected to gather over 20,000 community-based health workers to share experience of climate change impacts on health. Request your invitation here.)
Their answers paint a picture of the accelerating health crisis unfolding in the world’s most climate-vulnerable regions. Community nurses, doctors, midwives and public health officers detail how volatile weather patterns are driving up malnutrition, infectious disease, mental illness, and more – while simultaneously battering health systems and blocking patient access to care.
Yet woven throughout are also threads of resilience, ingenuity and hope. Health advocates are not just passively observing the impacts of climate change, but actively responding – often with scarce resources. From spearheading tree-planting initiatives to strengthening infectious disease surveillance to promoting climate literacy, they are innovating locally-tailored solutions.
Importantly, respondents emphasize that climate impacts cannot be viewed in isolation, but rather as one facet of the interlocking crises of environmental destruction, poverty, and health inequity. Their insights make clear that climate action and community health are two sides of the same coin – and that neither will be achieved without deep investment in local health workforces and systems.
Rooted in direct lived experience and charged with moral urgency, these frontline voices offer a stirring reminder that climate change is not some distant specter, but a life-and-death challenge already at the doorsteps of the global poor. As this new collection of insights implores, it’s high time their perspectives moved from the margins to the center of the climate debate.
As Charlotte Mbuh of The Geneva Learning Foundation explains: “We hope that the chorus of voices will grow to strengthen the case for why and how investment in human resources for health is likely to be a ‘best buy’ for community-focused efforts to build the climate resilience of public health systems.”
Jones, I., Mbuh, C., Sadki, R., & Steed, I. (2024). Climate change and health: Health workers on climate, community, and the urgent need for action (1.0). The Geneva Learning Foundation. https://doi.org/10.5281/zenodo.11194918
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#climateChange #communityHealth #health #HRH #HumanResourcesForHealth #localAction #TheGenevaLearningFoundation
Two weeks #PostCovid my sense of smell still hasn't come back.
I've been monitoring my #cardio #vitals (HR, RHR, HHR, O2 sat, BP (sys/dia)) and they're pretty good, despite inconsistent but persisting #fatigue - I tire easily. I haven't been "pushing" myself too much, but it doesn't take much to tire me out either.
My #HRH during "exertion" is slightly suspect. Lifting a 30lb suitcase was a near Herculean feat though my sleep has been off from travel which has an effect too, so who knows 🤷🏻♂️
The severe global shortage of health and care workers poses a dangerous threat to health systems, especially in low- and middle-income countries (LMICs). The authors of the article “Prioritising the health and care workforce shortage: protect, invest, together”, including six health ministers and the WHO Director-General, assert that this workforce crisis requires urgent action and propose “protect, invest, together” to tackle it.
Deep protection of the existing workforce, they assert, is needed through improved working conditions, fair compensation, upholding rights, addressing discrimination and violence, closing gender inequities, and implementing the WHO Global Health and Care Worker Compact to ensure dignified working environments. All countries must prioritize retaining workers to build resilient health systems.
Significantly increased and strategic long-term investments are imperative in both training new health workers through educational channels and sustaining their employment. Countries should designate workforce development, especially at the primary care level, as crucial human capital investments impacting population health outcomes. Intersectoral financing is key, bringing together domestic funds, grants, concessional sources, and private sector partners into coordinated national plans. Global solidarity is required to resource-constrained LMIC health workforces.
Intersectoral collaboration between ministries of health, finance, economic development, education and employment can develop integrated health workforce strategies. South-South partnerships offer pathways for health worker training and mobility to address regional shortages. Small island nations confront severe but overlooked workforce obstacles requiring specially tailored policy approaches.
The severe projected health workforce shortfall urgently necessitates that actors globally protect existing health workers, strategically invest in growing national workforces, and unite intersectorally behind robust health employment systems, especially in lower resourced contexts. As the authors emphasize, “there can be no health, health systems, or emergency response without the health and care workforce.”
What about the role of education?
This article does not provide much direct discussion of health education systems related to the global health workforce shortage. However, it makes the following relevant points:
Read more to understand what this means for health education: Protect, invest, together: strengthening health workforce through new learning models
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
Illustration: The Geneva Learning Foundation Collection © 2024
Who'd want to be king Charles' surgeon or anaesthetist.
#nopressure 😆
#KingCharles #HRH
