Equity matters: A practical approach to identify and eliminate biases

Patterns of prejudice: Connecting the dots helps health workers combat bias worldwide

Reda SadkiGlobal health

English | Français

“I noticed that every time he went to appointments or emergency services, he was often met with suspicion or treated as if he was exaggerating his symptoms,” shared a community support worker from Canada, describing how an Indigenous teenager waited three months for mental health services while non-Indigenous youth were seen within weeks.

This testimony was just one of hundreds shared during an unusual global gathering where frontline health workers confronted an uncomfortable truth: healthcare systems worldwide are riddled with biases that determine who lives and who dies.

“Equity Matters: A Practical Approach to Identify and Eliminate Biases,” a special event hosted by the Geneva Learning Foundation (TGLF) on 10-11 April 2025, drew nearly 5,000 health professionals from 72 countries. What made the event distinctive wasn’t just its scope, but its approach: creating a forum where community health workers from rural Nigeria could share insights alongside WHO officials from Switzerland, where district nurses from South Sudan could analyze cases with medical college professors from India.

When healthcare isn’t equal: Global patterns emerge

Despite working in vastly different contexts, participants described remarkably similar patterns of bias.

“A pregnant woman was about to deliver in the hospital, but the doctor said they need to deposit 500,000 naira before she can touch the woman,” recounted Onosi Chikaodiri Peter, a community health worker with Light Bringer’s Outreach in Nigeria. “The husband was begging, pleading, with 100,000 naira, telling the doctor that he could sell all his livestock to make sure that the wife was okay. But the doctor wouldn’t attend to the woman. Along the line, the woman gave up. The child died.”

Dr. Tusiime Ramadhan, who works with Humanitarian Volunteers International in Uganda, observed the same pattern: “People with money are referred to private clinics and hospitals for better health services often owned by the same government workers who sent them there.”

Some biases manifest in subtler ways. Hussainah Abba Ali, who works with Impact Santé Afrique in Cameroon, described seeking treatment for malaria during her university years: “Because I was a young woman, the nurse assumed I was just exaggerating. She barely examined me, gave me paracetamol and told me to rest. I later found out that several men who came in after me with similar symptoms were tested immediately for malaria.”

The stories came from everywhere—a physiotherapist in Nigeria whose expertise was ignored in favor of a male colleague; a nutritionist in DR Congo whose albino neighbor avoided vaccination clinics because of stigma; a public health specialist in Ethiopia’s Somali Region who explained how healthcare systems are designed for settled communities, leaving pastoralist populations behind.

Alina Onica, a psychologist with Romania’s Icar Foundation working with domestic violence survivors, noted: “Victims are often judged for ‘not leaving’ the abuser, as if staying means it’s not serious. This bias ignores the complex trauma and fear they live with every day.”

A framework for sense-making beyond single-issue analysis

What united these diverse testimonies was the application of the BIAS FREE Framework, a practical tool that helps identify and eliminate discriminatory patterns in health systems.

“Margaret Eichler and I started this work back in 1995 after developing some gender-based analysis tools,” explained Mary Anne Burke, the framework’s co-author. “We realized we had created something that could be applied to all social hierarchies. We’ve workshopped it on every continent but Antarctica and found it applicable everywhere.”

Unlike approaches that focus exclusively on gender, ethnicity, or disability, the BIAS FREE Framework examines how these factors intersect. Brigid Burke, a researcher who’s used and taught the framework for 15 years, explained how to identify three distinct problem types:

  • H problems: Where existing hierarchies are maintained
  • F problems: Where relevant differences between groups are ignored
  • D problems: Where different standards are applied to different groups

“It is easier to understand a hierarchy when you’re experiencing the oppression,” Burke told participants. “You can feel that you’re being treated in a way that takes away your dignity. It’s harder when you might be the one who is either consciously or unconsciously oppressing other people.”

During the event, participants first shared their own experiences, then began to analyze them using the framework. Abdoulie Bah, a regional Red Cross officer from The Gambia, offered his analysis: “Oppressive hierarchies suggest that certain groups experience more oppression than others, often leading to a competitive dynamic among marginalized groups.”

Solutions from the ground up

What distinguished this event from typical global health conferences was its emphasis on solutions developed by frontline workers themselves.

Dr. Orimbato Raharijaona, a medical doctor from Madagascar, described his team’s efforts to reach children in remote areas: “We prioritized areas with low vaccination coverage and strengthened birth follow-up to target zero-doses. Community dialogue helped raise awareness of the need for vaccination.”

In Mali, Bouréma Mounkoro, a public health medical assistant, discovered that simply rescheduling vaccination days to align with community availability dramatically improved coverage rates and reduced dropouts.

Dayambo Yendoukoua from Niger’s Red Cross developed an integrated approach addressing rural women’s exclusion from maternal care: “Women from villages and farming hamlets have three times less access to obstetric care than urban women. We grouped women into Mothers’ Clubs, provided literacy training, set up income-generating activities, and established traditional ambulances managed by women.”

This emphasis on community-based solutions resonated with Esther Y. Yakubu, a health worker with the Health and Development Support Programme in Nigeria: “This program will surely be of great value in the health sector. If put in place, it will make a huge difference and patients will receive quality treatment without any segregations.”

Practical action – not academic debates – to decolonize global health

The event itself embodied the principles it aimed to teach. Rather than positioning Western experts as authorities, TGLF structured the event to value diverse forms of expertise.

“Community health workers can see barriers that researchers miss. Global researchers spot patterns invisible at the local level. Policy makers understand system constraints that affect implementation,” explained Reda Sadki, TGLF’s Executive Director. “It’s when these perspectives connect that we find better solutions.”

On 24-25 April 2025, this community will reconvene to determine if there is enough interest and momentum to launch the Foundation’s Certificate peer learning programme for equity in research and practice. An inaugural course could be launched as early as June 2025.

“Your participation helps determine if we develop a full program on identifying and removing bias in health systems,” TGLF explained in its materials. “When more than 1,000 people participate, it shows enough interest to create a more comprehensive learning opportunity.”

The certificate program will bring together participants from across professional hierarchies—community health workers, district managers, national planners, and global researchers—creating a rare space where knowledge flows in all directions.

Across time zones and contexts, the conversation highlighted a shared understanding: addressing bias in healthcare isn’t just about fairness—it’s about survival. As Haske Akiti Joseph, a radiographer from Nigeria’s National Orthopaedic Hospital, reflected: “These issues are happening everywhere because governments will not provide free medical services to the people, and medical considerations come due to who you are, not based on priority.”

In a world where your chances of receiving timely, appropriate healthcare often depend on your gender, ethnicity, wealth, or location, the BIAS FREE Framework offers a practical way forward—one that begins with recognizing patterns of oppression that transcend borders and cultures.

Image: The Geneva Learning Foundation Collection © 2025

L’équité, ça compte: Une approche pratique pour identifier et éliminer les biais

L’équité compte: quand les soignants du monde entier témoignent des inégalités en santé

Reda SadkiGlobal health

English | Français

GENÈVE, le 11 avril 2025 – Une initiative internationale inédite a rassemblé près de 5000 professionnels de santé pour partager leurs expériences face aux discriminations dans l’accès aux soins

« Un enfant est mort parce que sa famille ne pouvait pas déposer 500 000 nairas [environ 300 francs suisses] avant le début des soins. Le père avait pourtant supplié qu’on s’occupe de l’enfant, proposant 100 000 nairas et promettant de vendre son bétail pour payer le reste. » Ce récit glaçant d’un professionnel de santé nigérian illustre la dure réalité des inégalités d’accès aux soins dont de nombreux témoignages ont été partagés lors d’un événement international consacré à l’équité en santé.

Le 11 avril dernier, la Fondation Apprendre Genève a créé un espace de dialogue sans précédent, rassemblant près de 5 000 professionnels de la santé de 72 pays, dont 1 830 francophones. Intitulé « L’équité compte: une approche pratique pour identifier et éliminer les biais », cet événement a permis à des médecins, infirmiers, agents de santé communautaires et autres acteurs du terrain de raconter, dans leurs propres mots, les discriminations qu’ils observent quotidiennement.

Des récits convergents malgré la diversité des contextes

« L’originalité de cette rencontre réside dans sa capacité à faire émerger des expériences habituellement invisibilisées », explique Reda Sadki, directeur exécutif de la Fondation. « Des praticiens qui n’ont jamais accès aux tribunes internationales ont pu témoigner des réalités qu’ils affrontent chaque jour. »

Ces témoignages, remarquablement similaires malgré la diversité des contextes, révèlent que le statut social détermine encore largement la qualité et la rapidité des soins. « Nous avions amené un enfant gravement malade à l’hôpital », raconte Neville Kasongo, du Corps des jeunes contre le paludisme en République démocratique du Congo. « Pendant que nous attendions plus de six heures, j’ai vu notre voisin arriver avec son enfant malade. Comme il avait des relations particulières dans cette institution, les cadres soignants se sont précipités pour s’occuper de son fils. Pour nous qui n’avions aucune connexion, quand ils sont finalement venus, l’enfant était déjà très affaibli. Une heure après, il est décédé. »

Brigitte Meugang, point focal du Programme élargi de vaccination au Cameroun, a observé un phénomène similaire lors d’une visite à l’hôpital: « J’avais un malade hospitalisé et je suis arrivée un peu en retard pendant les heures de visite. Le vigile m’a dit: “Tu n’entres pas parce que l’heure de visite est déjà passée.” Quelques minutes plus tard, un cousin militaire est arrivé en tenue. Le vigile a ouvert le portail et lui a dit d’entrer. » Quand elle a demandé pourquoi, on lui a répondu qu’il était en uniforme. C’est seulement après avoir présenté sa carte professionnelle qu’elle a été autorisée à entrer.

Les intervenants ont également souligné comment des groupes entiers sont systématiquement laissés pour compte. « Dans les zones de conflit au Burkina Faso, les femmes, les enfants et les personnes âgées déplacés subissent des violences basées sur le genre car leurs besoins spécifiques ne sont pas pris en compte », témoigne une spécialiste genre et inclusion sociale. « Les enfants souffrent de malnutrition, les femmes enceintes n’ont pas accès aux consultations prénatales, et les personnes âgées ne bénéficient pas de soins adaptés. »

Quand l’injustice touche même les soignants

Particulièrement frappants sont les témoignages de professionnels de santé ayant eux-mêmes subi des discriminations. Le Dr Balkissa Modibo Hama, coordonnatrice du programme mondial d’éradication de la poliomyélite pour l’OMS en Guinée, raconte: « Lors de l’accouchement de ma seconde fille, le personnel ne s’est pas occupé de moi jusqu’à ce que la sage-femme responsable arrive et leur dise qui j’étais. Soudain, tous se sont mobilisés autour de moi en me reprochant de ne pas m’être présentée. Après mon accouchement, j’ai convoqué tout le personnel pour les sensibiliser sur le fait qu’on ne devrait pas avoir besoin de dire qui on est pour recevoir des soins de qualité. »

Dans certains cas, c’est l’expérience personnelle de l’injustice qui a motivé l’engagement professionnel. « À 13 ans, j’ai accompagné ma mère à l’hôpital », poursuit le Dr Hama. « L’infirmière, qui connaissait ma mère, a voulu me faire passer avant une femme Bororo dont l’enfant était plus mal en point. J’ai refusé, mais j’ai ensuite constaté que cette femme et son enfant avaient été négligés. Cette expérience m’a profondément marquée et a motivé ma décision de devenir médecin. »

Christian Kpoyablé Clahin, infirmier en Côte d’Ivoire, a partagé un cas tragique: « Une femme est venue avec son enfant gravement malade. Elle n’avait pas d’argent pour payer les analyses. L’enfant a été mis à l’écart au laboratoire et cela a traîné jusqu’à ce qu’il soit trop tard. L’enfant est mort. J’ai interpellé le directeur de l’hôpital, mais les sanctions n’ont été que verbales. »

Des initiatives locales qui font la différence

Au-delà du constat, les participants ont partagé des solutions concrètes qu’ils ont développées face à ces inégalités. Arthur Fidelis Metsampito Bamlatol, coordinateur d’une association de santé au Cameroun, explique: « J’avais observé que les enfants Baka [pygmées] étaient insuffisamment vaccinés. Après avoir signalé ce problème au médecin-chef de district, nous avons cartographié les campements dans la forêt et institué des stratégies spéciales. Lors des campagnes suivantes, nous marchions parfois plusieurs heures à pied pour atteindre ces communautés isolées. »

D’autres adaptations créatives ont été mentionnées, comme celle rapportée par Bouréma Mounkoro, assistant médical au Mali: « Le planning des activités de vaccination n’était pas synchronisé avec la disponibilité de la communauté. Nous avons reprogrammé les jours de vaccination en tenant compte des réalités locales, ce qui a amélioré la couverture vaccinale et réduit considérablement les cas d’abandon. »

Pour Brice Alain Dakam Ncheuta, responsable de l’engagement communautaire à Médecins Sans Frontières au Niger, comprendre les dynamiques culturelles est essentiel: « Dans le Grand Sahel, pour réduire les biais dans la prise en charge des violences basées sur le genre, nous travaillons étroitement avec les leaders communautaires. Nous proposons des soins médicaux sans heurter la sensibilité culturelle, car cela fait partie de l’identité des personnes que nous accompagnons. »

Les solutions peuvent parfois être simples mais révolutionnaires, comme l’illustre l’initiative de Dayambo Yendoukoua, délégué de programme santé à la Croix-Rouge au Niger: « Dans les villages et hameaux agricoles, nous avons constaté que les femmes ont trois fois moins accès aux soins obstétricaux que les femmes urbaines. Nous avons créé des Clubs de Mères, offert des formations d’alphabétisation, mis en place des activités génératrices de revenus, et établi des ambulances traditionnelles gérées par les femmes elles-mêmes. »

Vers un partage de savoirs plus équitable

L’originalité de cet événement réside également dans sa méthodologie même. Plutôt que de suivre le schéma classique des conférences internationales où les experts occidentaux partagent leur savoir avec les praticiens du Sud, la Fondation Apprendre Genève a délibérément inversé cette logique. « Ce sont les professionnels de terrain qui ont pris la parole en premier », souligne Reda Sadki, directeur exécutif de la Fondation.

« Les agents de santé communautaire peuvent voir des obstacles que les chercheurs manquent. Les décideurs comprennent les contraintes systémiques qui affectent la mise en œuvre des politiques. C’est lorsque ces perspectives se connectent que nous trouvons de meilleures solutions », poursuit-il.

Pour faciliter l’analyse de ces expériences, Brigid Burke a accompagné la rencontre en tant que Guide. Burke est une chercheuse spécialisée dans le cadre BIAS FREE, un outil développé par Mary-Anne Burke et Margaret Eichler, permettant d’identifier différents types de biais. Cela a permis d’aller au-delà des constats en proposant une grille d’analyse des échanges entre participants qui ont constitué le cœur de la rencontre.

Le succès de cette approche pourrait conduire à la création d’un programme de formation international, dont le lancement sera discuté lors d’une nouvelle rencontre fin avril. « Nous souhaitons développer un espace où les connaissances circulent véritablement dans toutes les directions, plutôt que du Nord vers le Sud », précise M. Sadki.

La participation massive à cet événement – bien au-delà des attentes des organisateurs – témoigne d’un besoin urgent d’aborder ces questions. « Votre participation aide à déterminer si nous développons un programme plus complet sur ces questions », a expliqué la Fondation. « Quand près de 5000 personnes participent, cela montre qu’il y a suffisamment d’intérêt. »

« La meilleure stratégie pour corriger tous les biais reste l’installation partout dans nos pays d’une couverture maladie universelle », suggère le Dr Oumar Traoré, médecin de santé publique en Guinée. Une vision à laquelle fait écho Amadou Gueye, président du Malaria Youth Corps en Guinée: « Ces témoignages nous rappellent que l’équité en santé n’est pas qu’une question technique, mais aussi une question de justice fondamentale. »

Image: Collection de la Fondation Apprendre Genève © 2025

Why YouTube is obsolete

Why YouTube is obsolete: From linear video content consumption to AI-mediated multimodal knowledge production

Reda SadkiGlobal health, Learning

Does the educational purpose of video change with AI?

The purpose of video in education is undergoing a fundamental transformation in the age of artificial intelligence. This medium, long established in digital learning environments, is changing not just in how we consume it, but in its very role within the learning process.

Video has always been a problem in education

Video has always presented significant challenges in educational contexts. Its linear format makes it difficult to skim or scan content. Unlike text, which allows learners to quickly jump between sections, glance at headings, or scan for key information, video requires sequential consumption. This constraint has long been problematic for effective learning.

Furthermore, in many regions where our learners are based, internet access remains expensive, unreliable, or limited. Downloading or streaming video content can be prohibitively costly in terms of both data usage and time. The result is straightforward: few learners will watch educational videos, regardless of their potential value.

The bandwidth and attention divide

This reality creates a significant divide in educational access. While instructional designers and educators in high-resource settings continue to produce video-heavy content, learners in bandwidth-constrained environments have been systematically excluded from these resources. Even when videos are technically accessible, the time investment required to watch linear content often exceeds what busy professionals can allocate to learning activities.

Emergent AI platforms are scanning YouTube video transcripts to extract precisely what users need. This capability suggests a transformation for the role of video. YouTube and other video platforms are evolving into what might be called “interstitial processors”, mediating layers that support knowledge production and dissemination for subsequent extraction and analysis by both humans and machines.

A more inclusive workflow for knowledge extraction

This changing relationship with video content could enable more inclusive approaches to learning. When I discover a potentially valuable educational webinar, I now follow a structured approach to maximize efficiency and accessibility:

  1. Download the video file.
  2. Transcribe it using Whisper AI technology.
  3. Ask targeted questions to extract meaningful insights from the transcript.
  4. Request direct quotes as evidence of key points.

This method circumvents the traditional requirement to invest 60 minutes or more in viewing content that may ultimately offer limited value. More importantly, it transforms bandwidth-heavy video into lightweight text that can be accessed, searched, and processed even in low-connectivity environments.

I suspect that it is no accident that YouTube has recently placed additional restrictions on downloading videos from its platform.

Bridging the resource gap with AI

Current consumer-grade AI systems like Claude.ai have limitations: they cannot yet process full videos directly. For now, we are restricted to text-based interactions with video content, hence my transcription of downloaded content. However, this constraint will likely dissolve as AI capabilities continue to advance.

The immediate benefit is that this approach can help bridge the resource gap that has disadvantaged learners in bandwidth-constrained environments. By extracting the knowledge essence from videos, we could make educational content more accessible and equitable across diverse learning contexts.

The continuing value of educational video production

Despite these challenges, educational video production continues to be a relevant method for humans and machines that need a way to share what they know. Hence, what we are witnessing is not the diminishing relevance of educational video, but rather a transformation in how its knowledge value is extracted and utilized. The production of video content remains valuable. It is our methods of processing and consuming it that are evolving.

Aligning with effective networked learning theory

This shift aligns with contemporary understanding of effective learning. Research consistently demonstrates that passive consumption of information, whether through video or text, remains insufficient for meaningful learning. Genuine knowledge development emerges through active construction – the processes of questioning, connecting, applying, and adapting information within broader contexts.

The AI-enabled extraction of insights from video content represents a step toward more active engagement with educational materials – transforming passive viewing into targeted interaction with the specific knowledge elements most relevant to individual learning needs.

Knowledge networks trump media formats

Our experience with global learning networks demonstrates the importance of moving beyond media format limitations. When health professionals from diverse contexts share practices and adapt them to their specific environments, the medium of exchange becomes secondary to the knowledge being constructed.

AI tools that can extract and process information from videos help overcome the medium’s inherent limitations, turning static content into formats that can not only be read, viewed, or listened to – but that can also be remixed and fused with other sources. This approach allows learners to engage more directly with knowledge, freed from the constraints of linear consumption and bandwidth requirements.

Rethinking video as a dual-purpose knowledge production format

We are witnessing the development of new approaches to educational content where media exists simultaneously for direct human consumption and as structured data for AI processing. When the boundaries between content formats become increasingly permeable, with value residing not in the medium itself but in the knowledge that can be extracted and constructed from it.

Despite the consumption challenges, video remains an exceptional medium for content production that serves both humans and machines. For content creators, video offers unmatched richness in communicating complex ideas through visual demonstration, tone, and emotional connection.

What is emerging is not a devaluation of video creation but a transformation in how its knowledge is accessed. As AI tools evolve, video becomes increasingly valuable as a comprehensive knowledge repository where information is encoded in multiple dimensions – visual, auditory, and textual through transcripts.

This makes video uniquely positioned as a “dual-purpose” content format: rich and engaging for those who can consume it directly, while simultaneously serving as a structured data source from which AI can extract targeted insights.

In this paradigm, video production remains vital while consumption patterns evolve toward more efficient, personalized knowledge extraction.

The creator’s effort in producing quality video content now yields value across multiple consumption pathways rather than being limited to linear viewing

How to cite this article: Sadki, R. (2025). Why YouTube is obsolete: From linear video content consumption to AI-mediated multimodal knowledge production. Learning to make a difference. https://doi.org/10.59350/rfr2z-h4y93

References

Delello, J.A., Watters, J.B., Garcia-Lopez, A., 2024. Artificial Intelligence in Education: Transforming Learning and Teaching, in: Delello, J.A., McWhorter, R.R. (Eds.), Advances in Business Information Systems and Analytics. IGI Global, pp. 1–26. https://doi.org/10.4018/979-8-3693-3003-6.ch001

Guo, P.J., Kim, J., Rubin, R., 2014. How video production affects student engagement: An empirical study of MOOC videos, in: Proceedings of the First ACM Conference on Learning@ Scale Conference. ACM, pp. 41–50. https://doi.org/10.1145/2556325.2566239

Hansch, A., Hillers, L., McConachie, K., Newman, C., Schildhauer, T., Schmidt, P., 2015. Video and Online Learning: Critical Reflections and Findings from the Field. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2577882

Kumar, L., Singh, D.K., Ansari, M.A., 2024. Role of Video Content Generation in Education Systems Using Generative AI:, in: Doshi, R., Dadhich, M., Poddar, S., Hiran, K.K. (Eds.), Advances in Educational Technologies and Instructional Design. IGI Global, pp. 341–355. https://doi.org/10.4018/979-8-3693-2440-0.ch019

Mayer, R.E., Fiorella, L., Stull, A., 2020. Five ways to increase the effectiveness of instructional video. Education Tech Research Dev 68, 837–852. https://doi.org/10.1007/s11423-020-09749-6

Netland, T., Von Dzengelevski, O., Tesch, K., Kwasnitschka, D., 2025. Comparing human-made and AI-generated teaching videos: An experimental study on learning effects. Computers & Education 224, 105164. https://doi.org/10.1016/j.compedu.2024.105164

Salomon, G., 1984. Television is “easy” and print is “tough”: The differential investment of mental effort in learning as a function of perceptions and attributions. Journal of Educational Psychology 76, 647–658. https://doi.org/10.1037/0022-0663.76.4.647

Sun, M., 2024. An Intelligent Retrieval Method for Audio and Video Content: Deep Learning Technology Based on Artificial Intelligence. IEEE Access 12, 123430–123446. https://doi.org/10.1109/ACCESS.2024.3450920

Image: The Geneva Learning Foundation Collection © 2025

Chilling effect

Chilling effect

Reda SadkiGlobal health

We reached out to senior decision makers working in global health about the new Certificate peer learning programme for equity in research and practice.

Crickets.

One CEO wrote: “We aren’t currently in a position to enter into new strategic partnerships on the topic.”

The chilling effect is real.

Many organizations are retreating from publicly championing equity work—even those with deep commitments to fairness and inclusion.

But here’s the opportunity: While public discourse faces headwinds, meaningful work continues through trusted networks and communities of practice.

This is precisely when innovation in equity approaches accelerates—away from the spotlight but with profound impact.

The evidence is clear: health systems that neglect equity waste resources and deliver poorer outcomes.

When research excludes key populations or policies overlook certain communities, we all lose—through inefficiency, increased costs, and diminished impact.

This moment calls for courage from those who understand that equity is fundamental to effective health systems.

“The ultimate measure of a person is not where they stand in moments of comfort, but where they stand at times of challenge.” – Martin Luther King Jr.

If you’re still committed to this essential work, you’re not alone.

Question: How are you maintaining momentum on equity work during challenging times?

Image: The Geneva Learning Foundation Collection © 2025

MOOC completion rates in context

Online learning completion rates in context: Rethinking success in digital learning networks

Reda SadkiGlobal health, Learning

The comprehensive analysis of 221 Massive Open Online Courses (MOOCs) by Katy Jordan provides crucial insights for health professionals navigating the rapidly evolving landscape of digital learning. Her study, published in the International Review of Research in Open and Distributed Learning, examined completion rates across diverse platforms including Coursera, Open2Study, and others from 78 institutions. 

  • With median completion rates of just 12.6% (ranging from 0.7% to 52.1%), traditional metrics may suggest disappointment. Jordan’s multiple regression analysis revealed that while total enrollments have decreased over time, completion rates have actually increased. 
  • The data showed striking patterns in how participants engage, with the first and second weeks proving critical—after which the proportion of active students and those submitting assessments remains remarkably stable, with less than 3% difference between them. 
  • The research challenges common assumptions about “lurking” as a participation strategy and provides compelling evidence that course design factors significantly impact learning outcomes. 

These findings reveal important patterns that can transform how we approach professional learning in global health contexts.

Beyond traditional completion metrics

For global health epidemiologists accustomed to face-to-face training with financial incentives and dedicated time away from work, these completion rates might initially appear appalling. In traditional capacity building programswhere participants receive per diems, travel stipends, and paid time away from work. Outcomes such as “completion” are rarely measured. Instead, attendance remains the key metric. In fact, completion rates are often confused with attendance. From this perspective, even the highest MOOC completion rate of 52.1% could be interpreted as a dismal failure.

However, this interpretation fundamentally misunderstands the different dynamics at play in digital learning environments. Unlike traditional training where external incentives and protected time create artificial conditions for participation, MOOCs operate in the reality of participants’ everyday professional lives. They typically do not require participants to stop work in order to learn, for example. The fact that up to half of enrollees in some courses complete them despite competing priorities, no financial incentives, and no dedicated work time represents remarkable commitment rather than failure.

What drives completion?

The data reveals three significant factors affecting completion:

  1. Course length: Shorter courses consistently achieved higher completion rates
  2. Assessment type: Auto-grading showed better completion than peer assessment
  3. Start date: More recent courses demonstrated higher completion rates

The critical engagement period occurs within the first two weeks—after which participant behavior stabilizes. This insight aligns with what emerging networked learning approaches have demonstrated in practice.

Rather than judging digital learning by metrics designed for classroom settings, we must recognize that participation patterns reflect authentic integration with professional practice. The measure of success is not just how many complete the formal course, but how learning connects to real-world problem-solving and contributes to sustained professional networks.

Moving beyond MOOCs: Health learning networks

The Geneva Learning Foundation’s approach offers a distinctly different model from traditional MOOCs. While MOOCs typically deliver standardized content to individual learners who progress independently, the Foundation’s digital learning initiatives are fundamentally network-based and practice-oriented. Rather than focusing on content consumption, their approach creates structured environments where health professionals connect, collaborate, and co-create knowledge while addressing real challenges in their work.

These learning networks differ from MOOCs in several key ways:

  • Participants engage primarily with peers rather than pre-recorded content
  • Learning is organized around actual workplace challenges rather than abstract concepts
  • The experience builds sustainable professional relationships rather than one-time course completion
  • Assessment occurs through peer review and real-world application rather than quizzes or assignments
  • Structure is provided through facilitation and process rather than predetermined pathways

The Foundation’s experience with over 60,000 health professionals across 137 countries demonstrates that when learning is connected to practice through networked approaches, different metrics of success emerge:

  • Knowledge application: Practitioners implement solutions directly in their contexts
  • Network formation: Sustainable learning relationships develop beyond formal “courses”
  • Knowledge creation: Participants contribute to collective understanding
  • System impact: Changes cascade through health systems

Implications for global health training

For epidemiologists and health professionals designing learning initiatives, these findings suggest several strategic shifts:

  1. Modular design: Create shorter, more connected learning units rather than lengthy courses
  2. Real-world integration: Link learning directly to participants’ practice contexts
  3. Peer engagement: Provide structured opportunities for health workers to learn from each other
  4. Network building: Focus on creating sustainable learning communities rather than isolated training events

The future of professional learning

The research and practice point to a fundamental evolution in how we approach professional learning in global health. Rather than replicating traditional per diem-driven training models online, the most effective approaches harness the power of networks, enabling health professionals to learn continuously through structured peer interaction.

This perspective helps explain why seemingly low completion rates should not necessarily be viewed as failure. When digital learning is designed to create lasting networks of practice—where knowledge emerges through collaborative action—completion metrics capture only a fraction of the impact.

For health systems facing complex challenges that include climate change, pandemic response, and health workforce shortages, this networked approach to learning offers a promising path forward—one that transforms how knowledge is created, shared, and applied to improve health outcomes globally.

Reference

Jordan, K., 2015. Massive open online course completion rates revisited: Assessment, length and attrition. IRRODL 16. https://doi.org/10.19173/irrodl.v16i3.2112

Sculpture: The Geneva Learning Foundation Collection © 2025

What is complex learning

What is complex learning?

Reda SadkiGlobal health

Complex learning happens when people solve real problems instead of just memorizing facts.

Think about the difference between reading about how to ride a bicycle and actually learning to ride one.

You cannot learn to ride a bicycle just by reading about it – you need to practice, fall, adjust, and try again until your body understands how to balance.

Health challenges work the same way.

Reading about how to respond to a disease outbreak is very different from actually managing one.

Complex learning recognizes this difference.

5 key features of complex learning:

  1. Learning by doing: People learn best when they work on real problems they face in their jobs. Instead of just listening to experts, they actively try solutions, see what works, and adjust their approach.
  2. No single right answer: Complex learning deals with situations where there is no perfect solution that works everywhere. What works in one community might fail in another because of different resources, cultures, or systems.
  3. Adapting to local reality: Rather than following fixed steps, complex learning helps people adapt general principles to their specific situation. A rural clinic and an urban hospital might need different approaches even when dealing with the same disease.
  4. Connecting different types of knowledge: Complex learning brings together technical knowledge (facts and procedures) with practical wisdom (experience and judgment). Both are needed to solve real health challenges.
  5. Learning from mistakes: In complex learning, mistakes are valuable opportunities to learn, not failures to be hidden. When something doesn’t work, the question becomes “What can we learn from this?” rather than “Who is to blame?”

Why it matters for health work:

Most health challenges are complex problems. Disease outbreaks, vaccination campaigns, and health system improvements all require more than just technical knowledge. They require the ability to:

  • Adapt to changing situations
  • Work with limited resources
  • Coordinate with different groups
  • Solve unexpected problems
  • Learn from experience

Complex learning builds these abilities by engaging people with real challenges, supporting them as they try solutions, and helping them reflect on what they learn.

Unlike traditional training that assumes knowledge flows from experts to learners, complex learning recognizes that knowledge emerges through practice and experience. When health workers engage with complex learning, they don’t just know more – they become better problem-solvers capable of addressing the unique challenges in their communities.

What is networked learning

What is networked learning?

Reda SadkiGlobal health

Networked learning happens when people learn through connections with others facing similar challenges. Think about how market traders learn their business – not through formal classes, but by connecting with other traders, sharing tips, and learning from each other’s experiences. This natural way of learning through relationships is what networked learning tries to support.

5 key features of networked learning:

  1. Learning from peers: In networked learning, people learn as much or more from others doing similar work as they do from experts. A community health worker in one village might discover an effective way to increase vaccination rates that could help workers in other villages.
  2. Knowledge flows in all directions: Unlike traditional training where knowledge flows only from the top down, networked learning allows knowledge to move in all directions – from national programs to local clinics, between regions, and from local implementers up to policy makers.
  3. Connections create value: The relationships between people become valuable resources for solving problems. Having a network of colleagues to ask for advice or share experiences with helps everyone work more effectively.
  4. Crossing boundaries: Networked learning connects people who might not normally work together – like doctors, nurses, community health workers, and managers. These diverse connections bring together different perspectives and create new solutions.
  5. Building on existing relationships: People already learn from colleagues they trust. Networked learning strengthens these natural connections and creates new ones, expanding who people can learn from.

Why networked learning matters for health work:

Health systems are full of isolated practitioners who could benefit from each other’s knowledge:

  • A nurse who developed an effective patient education approach
  • A community health worker who found a way to reach remote households
  • A clinic manager who improved medicine supply systems
  • A doctor who adapted treatment guidelines for local conditions

Networked learning connects these isolated pockets of knowledge, allowing good ideas to spread and adapt across different contexts.

Unlike traditional training that pulls people away from their work for workshops, networked learning happens through ongoing connections that support everyday problem-solving. When health workers participate in networked learning, they gain access to a community of practice that continues to provide support long after formal training ends.

Networked learning doesn’t replace expertise, but it recognizes that valuable knowledge exists throughout the health system – not just at the top. By connecting this distributed knowledge, networked learning helps good practices spread more quickly and adapt more effectively to local needs.

Complex problems

What is a complex problem?

Reda SadkiGlobal health

What is a complex problem and what do we need to tackle it?

Problems can be simple or complex.

Simple problems have a clear first step, a known answer, and steps you can follow to get the answer.

Complex problems do not have a single right answer.

They have many possible answers or no answer at all.

What makes complex problems really hard is that they can change over time.

They have lots of different pieces that connect in unexpected ways.

When you try to solve them, one piece changes another piece, which changes another piece.

It is hard to see all the effects of your actions.

When you do something to help, later on the problem might get worse anyway.

You have to keep adapting your ideas.

To solve really hard problems, you need to be able to:

  • Think about all the puzzle pieces and how they fit, even when you don’t know what they all are.
  • Come up with plans and change them when parts of the problem change.
  • Think back on your problem solving to get better for next time.

The most important things are being flexible, watching how every change affects other things, and learning from experience.

Image: The Geneva Learning Foundation Collection © 2024

References

Buchanan, R., 1992. Wicked problems in design thinking. Design issues 5–21.

Camillus, J.C., 2008. Strategy as a wicked problem. Harvard business review 86, 98.

Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., Siemens, G., 2023. Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education: Artificial Intelligence 4, 100138. https://doi.org/10.1016/j.caeai.2023.100138

Rittel, H.W., Webber, M.M., 1973. Dilemmas in a general theory of planning. Policy sciences 4, 155–169.

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

Reda SadkiGlobal health

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

Peer learning through Psychological First Aid: New ways to strengthen support for Ukrainian children

Peer learning for Psychological First Aid: New ways to strengthen support for Ukrainian children

Reda SadkiWriting

This article is based on Reda Sadki’s presentation at the ChildHub “Webinar on Psychological First Aid for Children; Supporting the Most Vulnerable” on 6 March 2025. Learn more about the Certificate peer learning programme on Psychological First Aid (PFA) in support of children affected by the humanitarian crisis in Ukraine. Get insights from professionals who support Ukrainian children.

“I understood that if we want to cry, we can cry,” reflected a practitioner in the Certificate peer learning programme on Psychological First Aid (PFA) in support of children affected by the humanitarian crisis in Ukraine – illustrating the kind of personal transformation that complements technical training.

During the ChildHub “Webinar on Psychological First Aid for Children; Supporting the Most Vulnerable”, the Geneva Learning Foundation’s Reda Sadki explained how peer learning provides value that traditional training alone cannot deliver. The EU-funded program on Psychological First Aid (PFA) for children demonstrates that practitioners gain five specific benefits:

First, peer learning reveals contextual wisdom missing from standardized guidance. While technical training provides general principles, practitioners encounter varied situations requiring adaptation. When Serhii Federov helped a frightened girl during rocket strikes by focusing on her teddy bear, he discovered an approach not found in manuals: “This exercise helped the girl switch her focus from the situation around her to caring for the bear.”

Second, practitioners document pattern recognition across diverse cases. Sadki shared how analysis of practitioner experiences revealed that “PFA extends beyond emergency situations into everyday environments” and “children often invent their own therapeutic activities when given space.” These insights help practitioners recognize which approaches work in specific contexts.

Third, peer learning validates experiential knowledge. One practitioner described how simple acknowledgment of feelings often produced visible relief in children, while another found that basic physical comforts had significant psychological impact. These observations, when shared and confirmed across multiple practitioners, build confidence in approaches that might otherwise seem too simple.

Fourth, the network provides real-time problem-solving for urgent challenges. During fortnightly PFA Connect sessions, practitioners discuss immediate issues like “supporting children under three years” or “recognizing severe reactions requiring referrals.” As Sadki explained, these sessions produce concise “key learning points” summarizing practical solutions practitioners can immediately apply.

Finally, peer learning builds professional identity and resilience. “There’s a lot of trust in our network,” Sadki quoted from a participant, demonstrating how sharing experiences reduces isolation and builds a supportive community where practitioners can acknowledge their own emotions and challenges.

The webinar highlighted how this approach creates measurable impact, with practitioners developing case studies that transform tacit knowledge into documented evidence and structured feedback that helps discover blind spots in their practice.

For practitioners interested in joining, Sadki outlined multiple entry points from microlearning modules completed in under an hour to more intensive peer learning exercises, all designed to strengthen support to children while building practitioners’ own professional capabilities.

This project is funded by the European Union. Its contents are the sole responsibility of TGLF, and do not necessarily reflect the views of the European Union.

Illustration: The Geneva Learning Foundation Collection © 2025