The 2025 Lancet Countdown report has begun to acknowledge a critical, often-overlooked source of intelligence to build climate-resilient health systems: the health worker. By including testimonials from health workers alongside formal quantitative evidence, the Lancet cracks open a door, hinting at a world beyond globally standardized datasets. This is a necessary first step. However, the report’s framework for action remains a traditional, top-down model. It primarily frames the health workforce as passive recipients of knowledge—a group that must be “educated and trained” because they are “unprepared”, rather than build on existing evidence that points to health workers as leaders for climate-health resilience.
The 2025 report confirms that climate change’s assault on human health has reached alarming new levels.
Thirteen of 20 indicators tracking health threats are flashing red at record highs.
Heat-related mortality, now estimated at 546,000 deaths annually in the 2012-21 period, has climbed 63% since the 1990s.
Deaths linked to wildfire smoke pollution hit a new peak in 2024, while fossil fuel combustion overall remained responsible for 2.52 million deaths in 2022 alone.
This accelerating health crisis unfolds against a backdrop of faltering political will.
The report documents governmental retreats from climate commitments.
Yet, within this sobering assessment lies a quiet but potentially pivotal shift.
For the first time, the Countdown’s country profiles integrate direct testimonials from frontline health workers, explicitly acknowledging their “lived experiences as valuable evidence”.
It is a crucial opening, recognizing that globally standardized data alone cannot capture the full picture or tell the story.
The Countdown’s inclusion of health worker voices in its country profiles is laudable.
Schön argued that the problems of greatest human concern often lie in that swamp, requiring practitioners to rely on experience and intuition – what he termed “knowing-in-action”.
This promising step creates new possibilities.
When the reference global report on climate change and health sees the frontline, this illuminates the path to recognize those working there as agents and leaders capable of forging solutions.
However, the report’s dominant framework still positions the health workforce primarily on the receiving end of knowledge transfer.
Indicator 2.2.5 meticulously documents gaps in climate and health education, concluding that professionals are left “unprepared”.
The resulting recommendation?
Health systems must “[e]ducat[e] and train[…] the health workforce”.
This framing, while highlighting a genuine need, implicitly casts health workers as passive vessels needing to be filled, rather than as active knowers and problem-solvers.
Frontline health workers are already responding – adapting vaccination schedules during heatwaves, managing cholera outbreaks after floods, counseling communities on new health risks – because they must.
Their daily observations is distinct from “lived experience”, because of their formal health education.
The patterns that emerge could form a vital, real-time early warning system, detecting subtle shifts in disease patterns or community vulnerabilities even before formal surveillance systems register them.
Worse, it reflects an “epistemological injustice” where practical wisdom is systematically devalued.
Here lies the crucial disconnect.
The Lancet Countdown rightly presents evidence for “community-led action,” showcasing powerful examples in Panel 6 where farmers or local groups have driven substantial environmental and health gains.
Yet, it fails to connect this potential explicitly to the health workers embedded within those very communities.
What does empowering the health workforce truly mean?
It cannot be limited to providing didactic training, such as webinar lectures about climate science.
Drawing on our research and practice, it involves concrete actions:
Recognizing health professionals as knowledge creators: Systematically capturing, validating, and integrating their “knowing-in-action” into the evidence base.
Connecting them through peer learning networks: Enabling practitioners facing similar “swampy” problems across diverse contexts to share hyperlocal solutions and build collective intelligence.
Supporting locally-led implementation: Equipping them to design and execute adaptation projects tailored to community needs, often leveraging existing local resources, as demonstrated in TGLF initiatives where the vast majority of participants reported sustaining action without external funding.
Creating feedback loops to policy: Establishing mechanisms for this ground-level knowledge to flow upwards, informing district, national, and even global strategies.
This approach offers concrete pathways for the academic research community.
These networks function as distributed, real-world laboratories.
They generate rich qualitative and quantitative data on context-specific climate impacts, the practicalities of implementing adaptation strategies, barriers encountered, and observed outcomes.
They offer fertile ground for implementation science, participatory action research, and validating citizen science methodologies at scale.
Rigorous study of these networks themselves – how knowledge flows, how solutions spread, how collective capacity builds – can advance our understanding of learning and adaptation in complex systems.
This vision of an empowered, networked health workforce directly supports emerging global policy.
Peer learning networks provide a practical, field-tested engine to translate these principles into action, connecting the ambitions of Belém with the realities faced by a nurse in Bangladesh, a community health worker in Kenya, or a community health doctor in India.
Furthermore, this approach may represent one of the most effective investments available.
Preliminary analysis by The Geneva Learning Foundation suggests that supporting local action health workers through networked peer learning could yield substantial health gains.
With a critical mass of one million health workers connected to learn from and support each other, the potential is to save seven million lives, at a cost lower than that of immunization.
This is not just about doing good.
It is about smart investment in resilience.
The 2025 Lancet Countdown acknowledges the view from the ground.
The challenge now is to fully integrate that perspective into research and policy, by supporting and amplifying existing, community-led local action.
We must move beyond framing health workers as recipients of knowledge or vulnerable populations needing protection, and recognize their indispensable role as knowledgeable, capable leaders.
Harnessing their “knowing-in-action” through structured, networked peer support is not merely an alternative approach.
It is essential for building the adaptive, equitable, and effective health responses this escalating climate crisis demands.
The wisdom needed to navigate the swamp often resides within it.
References
Romanello M, Walawender M, Hsu S-C, et al. The 2025 report of the Lancet Countdown on health and climate change. Lancet 2025; published online Oct 29. https://doi.org/10.1016/S0140-6736(25)01919-1.
Sadki, R., 2025a. Climate change and health: a new peer learning programme by and for health workers from the most climate-vulnerable countries. https://doi.org/10.59350/redasadki.21339
Sadki, R., 2025b. WHO Global Conference on Climate and Health: New pathways to overcome structural barriers blocking effective climate and health action. https://doi.org/10.59350/redasadki.21322
Sadki, R., 2023a. Investing in the health workforce is vital to tackle climate change: A new report shares insights from over 1,200 on the frontline. https://doi.org/10.59350/3kkfc-9rb27
Sanchez, J.J., Gitau, E., Sadki, R., Mbuh, C., Silver, K., Berry, P., Bhutta, Z., Bogard, K., Collman, G., Dey, S., Dinku, T., Dwipayanti, N.M.U., Ebi, K., Felts La Roca Soares, M., Gudoshava, M., Hashizume, M., Lichtveld, M., Lowe, R., Mateen, B., Muchangi, M., Ndiaye, O., Omay, P., Pinheiro dos Santos, W., Ruiz-Carrascal, D., Shumake-Guillemot, J., Stewart-Ibarra, A., Tiwari, S., 2025. The climate crisis and human health: identifying grand challenges through participatory research. The Lancet Global Health 13, e199–e200. https://doi.org/10.1016/s2214-109x(25)00003-8
Schön, D.A., 1995. Knowing-in-action: The new scholarship requires a new epistemology. Change: The Magazine of Higher Learning 27, 27–34. https://doi.org/10.1080/00091383.1995.10544673
The Geneva Learning Foundation, 2023. On the frontline of climate change and health: A health worker eyewitness report. The Geneva Learning Foundation. https://doi.org/10.5281/ZENODO.10204660
Imagine hiring an assistant who never sleeps, never forgets, can work on a thousand tasks simultaneously, and communicates with you in your own language. Now imagine having not just one such assistant, but an entire team of them, each specialized in different areas, all coordinating seamlessly to achieve your goals. This is the “agentic AI revolution” —a transformation where AI systems become agents that can understand objectives, remember context, plan actions, and work together to complete complex tasks. It represents a shift from AI as a tool you use to AI as a workforce that you collaborate with.
Understanding AI agents: More than chatbots
When most people think of AI today, they think of ChatGPT or similar systems—you ask a question, you get an answer. That interaction ends, and the next time you return, you start fresh. These are powerful tools, but they are fundamentally reactive and limited to single exchanges.
AI agents are different. They work on a principle of “language in, memory in, language out.” Let’s break down what this means:
Language in: You describe what you want in natural language, not computer code. “Find me a house in California that meets these criteria…”
Memory in: The agent remembers everything relevant—your preferences, previous searches, budget constraints, past interactions. It maintains this memory across days, weeks, or months.
Language out: The agent reports back in plain language, explains what it did, and asks for clarification when needed. “I found three properties matching your criteria. Here’s why each might work…”
But here is the crucial part: between receiving your request and reporting back, the agent can take actions in the world. It can search databases, fill out forms, make appointments, send emails, analyze documents, and coordinate with other agents.
The house that agentic AI built
The example of building a house perfectly illustrates how agents transform complex projects. In the traditional approach, you would:
Spend weeks searching real estate listings yourself.
Hire a lawyer to research zoning laws and regulations.
Work with an architect to design the building.
Interview and select contractors.
Manage the construction process.
Deal with disputes if things go wrong.
Each step requires your active involvement, coordination between different professionals, and enormous amounts of time.
In the agentic model, you simply state your goal: “I want to build a house in California with these specifications and this budget.” Then:
Agent 1 searches for suitable lots, analyzing thousands of options against your criteria.
Agent 2 researches all applicable regulations, permits, and restrictions for each potential lot.
Agent 3 creates design options that maximize your preferences while meeting all regulations.
Agent 4 identifies and vets contractors, checking licenses, reviews, and past performance.
Agent 5 monitors construction progress and prepares documentation if issues arise.
These agents do not work in isolation. They communicate constantly:
The lot-finding agent tells the regulation agent which properties to research.
The regulation agent informs the design agent about height restrictions and setback requirements.
The design agent coordinates with the contractor agent about feasibility and costs.
All agents update you on progress and escalate decisions that need human judgment.
Why agentic AI changes everything
This workflow example is true of every business, every government, and every group human activity. In other words, this transformation has universal relevance.
Every complex human endeavor involves similar patterns:
Multiple steps that must happen in sequence;
Different types of expertise needed at each step;
Coordination between various parties;
Information that must flow between stages; and
Decisions based on accumulated knowledge.
Today, humans do all this coordination work. We are the project managers, the communicators, the information carriers, the decision makers at every level. The agentic revolution means AI agents can handle much of this coordination, freeing humans to focus on setting goals and making key judgments.
The memory advantage
What makes agents truly powerful is their memory. Unlike human workers who might forget details or need to be briefed repeatedly, agents maintain perfect recall of:
Every interaction and decision;
All relevant documents and data;
The complete history of a project; and
Relationships between different pieces of information.
This memory persists across time and can be shared between agents. When you return to a project months later, the agents remember exactly where things stood and can continue seamlessly.
Agentic AI from individual tools to digital teams
The revolutionary aspect is not just individual agents but how they work together. Like a well-functioning human team, AI agents can:
Divide complex tasks based on specialization;
Share information and coordinate actions;
Escalate issues that need human decision-making;
Learn from outcomes to improve future performance; and
Scale up or down based on workload.
But unlike human teams, they can:
Work 24/7 without breaks;
Handle thousands of tasks in parallel;
Communicate instantly without misunderstandings;
Maintain perfect consistency; and
Never forget critical details.
The new human role as co-worker to agentic AI
In this world, humans do not become obsolete—our role fundamentally changes. Instead of doing routine coordination and information processing, we:
Set goals and priorities;
Make value judgments;
Handle exceptions requiring creativity or empathy;
Build relationships and trust;
Ensure ethical considerations are met; and
Provide the vision and purpose that guides agent actions.
Challenges and considerations
The agentic revolution raises important questions:
Trust: How do we verify agents are acting in our interest?
Control: What happens when agents make decisions we did not anticipate?
Accountability: Who is responsible when an agent makes an error?
Privacy: What data do agents need access to, and how is it protected?
Employment: What happens to jobs based on coordination and information processing?
What can agentic AI do in 2025?
Early versions of these agents already exist in limited forms. Organizations and individuals who understand this shift early will have significant advantages. Those who continue operating as if human coordination is the only option may find themselves struggling to compete with those augmented by agentic AI teams.
Where do we go from here?
The agentic revolution represents something humanity has never had before: the ability to multiply our capacity for complex action without proportionally increasing human effort. It is as if every person could have their own team of tireless, brilliant assistants who understand their goals and work together seamlessly to achieve them.
This is not about replacing human intelligence but augmenting human capability. When we can delegate routine coordination and information processing to agents, we can focus on what humans do best: creating meaning, building relationships, making ethical judgments, and pursuing purposes that matter to us.
The world we imagine—where building a house or running a business or navigating healthcare becomes as simple as stating your goal clearly—represents a fundamental shift in how complex tasks get accomplished. Whatever the timeline for this transformation, understanding how AI agents work and what they make possible has become essential for anyone trying to make sense of where our societies are heading.
The concept is clear: AI systems that can understand goals, remember context, and coordinate actions to achieve complex outcomes. What we do with this capability remains an open question—one that will be answered not by the technology itself, but by how we choose to use it.
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:
Offering simplified, mobile-friendly courses will make training more accessible to health workers.
Incorporating videos and case studies will keep learners engaged.
Quizzes and knowledge checks will ensure learning happens.
Certificates, continuing education credits, and small incentives will motivate course completion.
Growing the user base through marketing and partnerships is the path to impact.
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.
The following is excerpted from 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.
This chapter’s final example illustrates the way in which organically arising IIL (informal and incidental learning) is paired with opportunities to build knowledge through a combination of structured education and informal learning by peers working in frequently complex circumstances.
Reda Sadki, president of The Geneva Learning Foundation (TGLF), rethought learning and development (L&D) for immunization workers in many roles in low- and middle-income countries (LMICs).
Adapting to technology available to participants from the countries that joined this effort, Sadki designed a mix of experiences that broke out of the limits of “training” as it was often designed by conventional learning and development practitioners.
He addressed, the inability to scale up to reach large audiences; difficulty to transfer what is learned; inability to accommodate different learners’ starting places; the need to teach learners to solve complex problems; and the inability to develop sufficient expertise in a timely way. (Marsick et al., 2021, p. 15)
This led his organization, to invite front-line staff from all levels of immunization systems in low- and middle-income countries (LMICs) to create and share new learning in response to the social and behavioral challenges they faced.
Sadki designed learning and development for “in-depth engagement on priority topics,” insights into “the raw, unfiltered perspectives of frontline staff,” and peer dialogue that “gives a voice to front-line workers” (The Geneva Learning Foundation, 2022).
Reda started with an e-learning course, which he supplemented by interactive, community building, and knowledge creation features offered by Scholar, a learning platform developed by Bill Cope and Mary Kalantzis (Marsick et al., 2021, pp. 185-186).
Scholar’s learning analytics enabled him to tailor learning to learner preferences and to continually check outcomes and adjust next steps.
See Figure 4.3, which lays out the full learning cycle, a combination of interventions that Reda assembled over time to support peer learning-based work—“work that privileges learning in order to build individual and organizational capacity to better address emergent challenges or opportunities” (Marsick et al., 2021, p.177).
In his initiative, over a period of 12-18 months, participants develop and implement projects related to local immunization initiatives.
To date, participants have come from 120 countries.
In this vignette, Reda Sadki reflects on how this new model for learning and development evolved over time, and how L&D is transformed in a connected, networked learning environment.
My reframe of learning and development started when I wrote to Bill Cope and Mary Kalantzis, respectively professor and dean of the University of Illinois College of Education, after I was appointed Senior Officer for Learning Systems at the International Federation of Red Cross and Red Crescent Societies (IFRC). I shared my strategy for the organization of facilitation, learning, and sharing of knowledge. I thought my strategy was brilliant. (At the time, I was already thinking that this was about more than learning and development…)
They replied that these were interesting ideas, but I was missing the point because this is not learning. What I shared focused on publishing knowledge in different ways, but not on creation of knowledge as key to the learning process.
That was a shock to me.
So, the first realization about the limits of current thinking about learning and development came from Bill and Mary challenging me by saying: “What are people actually getting to do? You know, that’s where the learning is likely to happen.”
I could see they had a point, but I didn’t know what it meant.
I reflected on recent work I had done for the IFRC, where I was responsible for a pipeline of 80 or so e-learning modules.
These information transmission modules were extremely limited, had very little impact.
But there is a paradox, which is that people across the Red Cross who we were trying to reach were really excited and enthusiastic about them.
I had not designed these modules.
It was 500 screens of information with quizzes at the end.
It violated every principle of learning design.
And yet people loved it and were really proud to have completed it.
The second realization was that what made people excited using the most boring format and medium was that this was the first time in their life that they were connecting in a digital space with something that spoke to their IFRC experience.
So, the driver was learning.
People come to the Red Cross and Red Crescent because they want to learn first aid skills, to prepare for a disaster, or to recover from one.
Previously, that was an entirely brick-and-mortar experience.
You have Red Cross branches pretty much everywhere in the world.
It’s a very powerful social peer learning experience.
The trainer teaching you is likely to be someone like you from your community.
You meet people with like-minded values.
And so, however inadequate, the digital parallel to that existed, and it helped people connect with their Red Cross culture, but in a digital space.
With that insight, the learning platform became the fastest-growing digital system in the entire Red Cross Red Crescent Movement.
That was the connection of learning and development to complexity and networks.
I read Marsick and Watkins in the ’80s and ’90s. Informal and incidental learning mattered then. Its significance would explode with the digital transformation.
In my mind , that is what Siemens tapped into in the 2000s, through the lenses of digital network, complexity, and systems theory.
The Internet leads to a different kind of thinking and doing.
His theory of learning, connectivism, grew out of that difference.
January of 2011, Ivy League universities began to publish massive open online courses (MOOCs), three years after George Siemens and his Canadian colleagues had coined the term while implementing connectivism.
Stanford professors had 150,000 people in their artificial intelligence MOOC, alongside 400 people who took the same course on the Stanford campus.
Learning at scale is an important part of problem-solving complex challenges.
It is also important for peer learning and innovation: the greater the scale, the greater the diversity of inputs that we can use to support each other’s learning.
Nine years later, at the Geneva Learning Foundation, we had digital scaffolding or learning infrastructure already in place.
I had been working, since 2016, with the World Health Organization, to help country-based immunization staff translate global guidelines, norms, and standards into practice.
The COVID-19 Scholar Peer Hub became a digital network hosted by The Geneva Learning Foundation (TGLF) and developed with over 600 health worker alumni from all over the world.
We began to understand not only learningat scale, but also design at scale.
The Peer Hub launched in July 2020 and connected over 6,000 health professionals from 86 countries to contribute to strengthening skills and supporting implementation of country COVID-19 plans of action for vaccination, and to recover from the damage wrought by the pandemic.
Our network, platform, and community tripled in size, in less than six months.
Using social network analysis (SNA), Sasha Poquet explored the value of such a learning environment, one that builds a community of learning professionals, and that has ongoing activities to maintain the community both short- and long term, where you educate through various initiatives rather than create individual communities for each independent offering.
It’s a holistic system of systems, in which everything is connected to everything, and every component is like a fractal embedded in the other components.
This is not an abstract concept. We have found ways to actually implement this, in practical ways, with startling outcomes.
That’s where we have moved in rethinking learning and development.
You help people learn by connecting to each other, and by understanding the informal, incidental nature of learning.
Yet these are two competing frameworks that collide, contradict, and are superimposed on top of each other.
Both are helpful at specific times.
In general, you can recognize the tensions and say: “Well, let’s put each one in front of the problem. Let’s see what we gain by applying each. Let’s reconcile in situ what the contradictory things are that we learn through these different lenses and then make decisions and figure out what the design elements look like.”
What does it give to hold these notions of community and network in creative tension with one another?
It’s kind of like a fruit salad where you mix all these fruits together and the juice you get at the bottom of the bowl tends to be really delicious. That’s the best case.
The flip side can be confusion.
Some categories of learners just feel completely overwhelmed by being presented with multiple ways of doing something, having to make their own decisions in ways they’re simply not used to, being given too many choices or being put in contexts that are too ambiguous for there to be an easy resolution.
But if you think about the skills we need in a digital age—for navigating the unknown, accepting uncertainty, making decisions, that ability to look around the corner—we try to convey the message to people who are uncomfortable that if they don’t figure out how to overcome their discomfort, they’re probably going to struggle and not be ready to function in the age in which we live.
Evolution of a new model for learning and development
Looking back to early 2020, Reda described important insights from an early pre-course symposium offering lived experiences shared by course applicants combined with video archives drawn from prior conferences sponsored by the Bill & Melinda Gates Foundation.
Reda packaged selected recorded talks in a daily sequence, and interspersed it with networking discussions and sharing of experiences of immunization training by field-based practitioners.
For many, it was the first time they could go online and discover the experience of a peer, who could be from anywhere in the world.
It was a process of discovery – realizing you can literally and figuratively connect across distance with people who are like yourself.
We were able to create a conference-like experience, a metaphor that’s familiar to many—the combination of presentation and conversation and shared experience – by basically Scotch-taping together some older videos and editing a few stories from the real world.
Now, it was part of an overall process over several years that got us to that point—where we had formed a community, a digital community that was mature enough, that was sophisticated enough, to overcome the barriers they were facing and participate.
But still, it showed it could be done.
We began to try out our new ideas and practices.
In the first Teach to Reach Conference in January 2021, we designed with an organizing committee composed of over 500 alumni, we set up opportunities for people to pair of and talk to one another about their field experiences with vaccination.
Peer learning mattered more than ever, because participants were immunization staff getting ready to introduce new COVID-19 vaccines in developing countries.
There were no established norms and standards for how to do this.
The conference offered some 56 workshops and other formal sessions, plenaries, and interviews.
However, we discovered that the most meaningful learning was through some 14,000 networking meetings, where you pressed a button and you were randomly matched with someone else at the conference.
People now join group sessions where you listen to peers sharing their insights and experiences of vaccine hesitancy or other topics, and then you go off and network in one-to-one, private meetings and share your own experience, nourished by what happened in that group session; and also continue your learning in that very intimate way that you get through individual conversation that you don’t get in the anonymization of the Zoom rectangles.
Dialogue is great, but we are most interested in action that leads to results.
In every formal course, learners design a project around a real problem that they face, and use multiple learning resources to support learning in the context of that project.
An evaluation showed that people were already implementing projects and doing things with what they had learned.
How could we scaffold not just learning but actual project implementation?
In order to catalyze action, we added a number of components in a sequence, a deliberate pedagogical pattern designed on the basis of evidence from learning science combined with empirical evidence from our practice.
First, the Ideas Engine, where people share ideas and practices, and give and receive feedback on them.
That’s followed by situation analysis really getting to the root cause of the problem they’re facing. We just ask learners to ask “why” fives times. Half of learners found a root cause different from the one they had initially diagnosed.
And third, then, is action planning to clarify: What’s your goal? What are three corrective actions you’re going to take? How will you know that you have achieved your goal?
These are classic, conventional action planning questions.
The difference is the networked, peer learning model. It’s described by some learners as a “superpower”. Defying distance and many other boundaries, each person can tap into collective intelligence to accelerate their progress.
It has taken years to bring together the right components, in the right sequence, to encourage reflective practice, develop analytical competencies, higher-order learning… but in ways that link every step of thinking to doing, and where the end game is about improved health outcomes, not just learning outcomes.
That led us ultimately to the Impact Accelerator—that doesn’t have an end point.
It starts with four weeks of goal setting, focused on continuous quality improvement.
People initially declare very ambitious goals like, “By the end of the month I will have improved immunization coverage.” This is too broad to be useful, and seldom can be achieved within a month.
We help them set specific goals. For example: “By the end of the month, I will have presented the project to my boss and secured some funding”— and even that may be quite ambitious.
We help people figure out for themselves what they can actually do within the constraints they have.
Unlike “Grand Challenges” or other innovation tournaments, you don’t have a competitive element, you don’t have a financial incentive, and it still works.
The heart and soul of it is intrinsic motivation.
After these steps there’s ongoing longitudinal reporting.
Peer learning provides a new kind of accountability, as colleagues challenge each other to do better – and also to present credible results.
Basically, we’ll call you back and ask, what happened to that project you were doing? Did you finish it? Did you get stuck? if so, why? What evidence do you have that it’s made a difference? You share that with us and if you have good news to share, we’ll probably invite you to an inspirational event for the next cycle.
Challenges in inventing a new learning model
If you look at this from the point of view of the learner, the first point of contact is social.
It’s somebody they know who’s going to share with them on WhatsApp the invitation to join the program.
Second are steps that test motivation and commitment because they could be seen as barriers to entry, for example, a long questionnaire for the current full learning cycle.
To join the cycle, 6,185 people in the first two weeks took the time to answer 95 questions, generating over half a million data points and insights.
About 40% of people who start the questionnaire finish it, and then start receiving instructions in a flow of emails, to prepare for the next steps.
We could have reduced the number of questions, lowering the barrier to entry.
We start with didactic steps, combined with some inspirational messages, e.g., asking them to reflect on why they are committed to the program, or how they are going to organize their time.
We don’t know what the program design will look like until we’ve collected the applications and analyzed what people share about their biggest challenges because it’s all challenge-based.
For example, we may think there is a problem due to vaccine hesitancy. We may be right: vaccine hesitancy is frequently given as a significant challenge. But there may be some things that surprise us.
And so, we adapt every part of the design, and we keep doing that every day throughout the program, so there’s no disconnect between the design and the implementation.
The design is the content.
The first thing may be an inspirational event to connect with their intrinsic motivation, which we then tap into throughout the cycle.
In June 2022, for example, we had an event for the network that completed the first part of the full learning cycle.
We challenged people to share photos, showing them in the field, doing their daily work during World Immunization Week.
We received over 1,000 photos in about two weeks.
We organized a community event. It was a slide show: showing photos with music, reading the names of those who had contributed, inviting them to comment each other’s photos.
A big chunk of what we do addresses the affective domain of learning that is critical to complex problem-solving and usually incredibly hard to get to.
And what we saw were people in the room having those moments of coming to consciousness, realizing their problems are shared, and feeling stronger because of it.
It was online, but you could feel the emotion. Something very powerful that we do not quite know how to describe, measure, or evaluate.
People love peer learning in principle but still are wary.
They might wonder how they can trust what their peer says: What’s the proof I can rely on them? What happens if they let me down? How do I feel if I don’t own up to the expectations? What if I’m peer-reviewing the work of somebody who’s far more experienced than I am, or conversely, if I read somebody’s work and judge they didn’t have the time or make the effort to do something good?
We use didactic constraints to scaffold spaces of possibility: If your project is due by Friday, we announce that there will be no extension. By contrast, the choice of project is yours.
We’re not going to tell you what your challenge is in your remote village, so you define it. We will challenge you to put yourself to the test, to demonstrate that this is actually your toughest challenge.
Or to demonstrate that what you think is the cause is the actual root cause.
And then we’ll have a support system that has about 20 different ways in which people can not only receive support, but also give it to others.
For the technical support sessions, for example, we’ll say there are two reasons for joining. Either you have a technical issue you want to solve; or you’re doing so well, you have a little bit of time to give to help your colleagues.
This is just one example of how we encourage connections between peers.
It took us years to find the right way to formulate the dialectic between those who are doing well, and those who are not. Are they really peers?
Over time, we gained confidence in peer learning after we adopted it.
We had prior experiences with learners who wanted an expert to tell them if their assignment was good or not.
Getting people to trust peer learning forced us to think through how we articulate the value of peer learning.
How do we help people understand that the limitations are there, but that they do not limit the learning?
An assumption in global health is that, in order to teach, you need technical expertise.
So if you are a technical expert, it is assumed that you can teach what you know.
We consider subject matter expertise, but if you are an expert and come to our event, you’re actually asked to listen, as a guide on the side rather than a sage on the stage.
You do not get to make a presentation, at least not until learners have experienced the power of peer leraning.
You listen to what people are sharing about their experiences.
Then, you have a really important role, that is to respond to what you’ve heard and demonstrate that your expertise is relevant and helpful to people who are facing these challenges.
That has sometimes led to opposition when experts realize to what extent we flipped the prevailing model around.
Some people really embrace it.
Others get really scared.
One of the most recent shifts we have made is that we stopped talking about courses.
Courses are a very useful metaphor, but we are now talking about a movement for immunization.
In the past, we observed that people who dropped out felt shame and stopped participating.
Even if you are not actively participating, you’re still a member of the immunization movement.
People have participated as health professionals, as government workers, as members of civil society, in various kinds of movements since decolonization.
So the “movement” metaphor has a different resonance than that of “courses”.
We used to call the Monday weekly meeting a discussion group.
We’re now calling it a weekly assembly.
It is a term that speaks to the religiosity of many learners, as well as to those with social commitments in their local communities.
About ten years ago, I began to think of my goal for these discussion groups like the musician, the artist that you most appreciate, who really moves your soul, moves you, your every fiber and your body and your soul and your mind.
I remember in 1989 I went to a Pink Floyd concert.
When we left the concert, we were drenched in sweat.
I was exhausted and just had an exhilarating experience.
That’s what I would like people who participate in our events to feel.
I believe that’s key to fostering the dynamics that will lead to effective teaching and learning and change as an outcome.
We’re still light years away from that.
A global health researcher told me that when she joins our events, she feels like she is in church in her home country of Nigeria.
