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Tag: implementation science

  • Why guidelines fail: on consequences of the false dichotomy between global and local knowledge in health systems

    Why guidelines fail: on consequences of the false dichotomy between global and local knowledge in health systems

    Global health continues to grapple with a persistent tension between standardized, evidence-based interventions developed by international experts and the contextual, experiential local knowledge held by local health workers. This dichotomy – between global expertise and local knowledge – has become increasingly problematic as health systems face unprecedented complexity in addressing challenges from climate change to emerging diseases.

    The limitations of current approaches

    The dominant approach privileges global technical expertise, viewing local knowledge primarily through the lens of “implementation barriers” to be overcome. This framework assumes that if only local practitioners would correctly apply global guidance, health outcomes would improve.

    This assumption falls short in several critical ways:

    1. It fails to recognize that local health workers often possess sophisticated understanding of how interventions need to be adapted to work in their contexts.
    2. It overlooks the way that local knowledge, built through direct experience with communities, often anticipates problems that global guidance has yet to address.
    3. It perpetuates power dynamics that systematically devalue knowledge generated outside academic and global health institutions.

    The hidden costs of privileging global expertise

    When we examine actual practice, we find that privileging global over local knowledge can actively harm health system performance:

    • It creates a “capability trap” where local health workers become dependent on external expertise rather than developing their own problem-solving capabilities.
    • It leads to the implementation of standardized solutions that may not address the real needs of communities.
    • It demoralizes community-based staff who see their expertise and experience consistently undervalued.
    • It slows the spread of innovative local solutions that could benefit other contexts.

    Evidence from practice

    Recent experiences from the COVID-19 pandemic provide compelling evidence for the importance of local knowledge. While global guidance struggled to keep pace with evolving challenges, local health workers had to figure out how to keep health services going:

    • Community health workers in rural areas adapted strategies.
    • District health teams created new approaches to maintain essential services during lockdowns.
    • Facility staff developed creative solutions to manage PPE shortages.

    These innovations emerged not from global technical assistance, but from local practitioners applying their deep understanding of community needs and system constraints, and by exploring new ways to connect with each other and contribute to global knowledge.

    Towards a new synthesis

    Rather than choosing between global and local knowledge, we need a new synthesis that recognizes their complementary strengths. This requires three fundamental shifts:

    1. Reframing local knowledge

    • Moving from viewing local knowledge as merely contextual to seeing it as a source of innovation.
    • Recognizing frontline health workers as knowledge creators, not just knowledge recipients.
    • Valuing experiential learning alongside formal evidence.

    2. Rethinking technical assistance

    • Shifting from knowledge transfer to knowledge co-creation.
    • Building platforms for peer learning and exchange.
    • Supporting local problem-solving capabilities.

    3. Restructuring power relations

    • Creating mechanisms for local knowledge to inform global guidance.
    • Developing new metrics that value local innovation.
    • Investing in local knowledge documentation and sharing.

    Practical implications

    This new synthesis has important practical implications for how we approach health system strengthening:

    Investment priorities

    • Funding mechanisms need to support local knowledge creation and sharing
    • Technical assistance should focus on building local problem-solving capabilities
    • Technology investments should enable peer learning and knowledge exchange

    Capacity building

    Knowledge management (KM)

    New paths forward

    Moving beyond the false dichotomy between global and local knowledge opens new possibilities for strengthening health systems. By recognizing and valuing both forms of knowledge, we can create more effective, resilient, and equitable health systems.

    The challenges facing health systems are too complex for any single source of knowledge to address alone. Only by bringing together global expertise and local knowledge can we develop the solutions needed to improve health outcomes for all.

    References

    Braithwaite, J., Churruca, K., Long, J.C., Ellis, L.A., Herkes, J., 2018. When complexity science meets implementation science: a theoretical and empirical analysis of systems change. BMC Med 16, 63. https://doi.org/10.1186/s12916-018-1057-z

    Farsalinos, K., Poulas, K., Kouretas, D., Vantarakis, A., Leotsinidis, M., Kouvelas, D., Docea, A.O., Kostoff, R., Gerotziafas, G.T., Antoniou, M.N., Polosa, R., Barbouni, A., Yiakoumaki, V., Giannouchos, T.V., Bagos, P.G., Lazopoulos, G., Izotov, B.N., Tutelyan, V.A., Aschner, M., Hartung, T., Wallace, H.M., Carvalho, F., Domingo, J.L., Tsatsakis, A., 2021. Improved strategies to counter the COVID-19 pandemic: Lockdowns vs. primary and community healthcare. Toxicology Reports 8, 1–9. https://doi.org/10.1016/j.toxrep.2020.12.001

    Jerneck, A., Olsson, L., 2011. Breaking out of sustainability impasses: How to apply frame analysis, reframing and transition theory to global health challenges. Environmental Innovation and Societal Transitions 1, 255–271. https://doi.org/10.1016/j.eist.2011.10.005

    Salve, S., Raven, J., Das, P., Srinivasan, S., Khaled, A., Hayee, M., Olisenekwu, G., Gooding, K., 2023. Community health workers and Covid-19: Cross-country evidence on their roles, experiences, challenges and adaptive strategies. PLOS Glob Public Health 3, e0001447. https://doi.org/10.1371/journal.pgph.0001447

    Yamey, G., 2012. What are the barriers to scaling up health interventions in low and middle income countries? A qualitative study of academic leaders in implementation science. Global Health 8, 11. https://doi.org/10.1186/1744-8603-8-11

  • How can we reliably spread evidence-based practices at the speed and scale modern health challenges demand?

    How can we reliably spread evidence-based practices at the speed and scale modern health challenges demand?

    At a symposium of the American Society for Tropical Medicine and Hygiene (ASTMH) Annual Meeting, I explored how peer learning could help us tackle five critical challenges that limit effectiveness in global health.

    1. Performance: How do we move beyond knowledge gains to measurable improvements in health outcomes?
    2. Scale and access: How do we reach and include tens of thousands of health workers, not just dozens?
    3. Applicability: How do we ensure learning translates into changed practice?
    4. Diversity: How do we leverage different perspectives and contexts rather than enforce standardization?
    5. Complexity: How do we support locally-led leadership for change to tackle complex challenges that have no standard solutions?

    For epidemiologists working on implementation science, peer learning provides a new path for solving one of global health’s most persistent challenges: how to reliably spread evidence-based practices at the speed and scale modern health challenges demand.

    The evidence suggests we should view peer learning not just as a training approach, but as a mechanism for viral spread of effective practices through health systems.

    How do we get to attribution?

    Of course, an epidemiologist will want to know if and how improved health outcomes can be attributed to peer learning interventions.

    The Geneva Learning Foundation (TGLF) addresses this fundamental challenge in implementation science – proving attribution – through a three-stage process that combines quantitative indicators with qualitative validation.

    The process begins with baseline health indicators relevant to each context (such as vaccination coverage rates, if it is immunization), which are then tracked through regular “acceleration reports” that capture both metrics and implementation progress.

    Rather than assuming causation from correlation, participants must explicitly rate the extent to which they attribute observed improvements to their intervention.

    The critical innovation comes in the third stage: those claiming attribution must “prove it” to the community of peers, by providing specific evidence of how their actions led to the observed changes – a requirement that both controls for self-reporting limitations and generates rich qualitative data about implementation mechanisms.

    This methodology has proven particularly valuable in complex interventions where randomized controlled trials may be impractical or insufficient.

    What are examples of peer learning in action?

    Here are three examples from The Geneva Learning Foundation’s work that demonstrate scale, reach, and sustainability.

    Within four weeks, a single Teach to Reach cohort of 17,662 health workers across over 80 countries generated 1,800 context-specific experiences describing the “how” of implementation, especially at the district and community levels.

    In Côte d’Ivoire, working with Gavi and The Geneva Learning Foundation, the national immunization team used TGLF’s model to support community engagement. Within two weeks, over 500 health workers representing 85% of the country’s districts had begun implementing locally-led innovations. 82% of participants said they would use TGLF’s model for their own needs, without requiring any further assistance or support.

    In TGLF’s COVID-19 Peer Hub, 30% of participants successfully implemented recovery plans within three months – a rate seven times higher than a control group that did not use TGLF’s model.

    Participants who actively engaged with peers were not only more likely to report successful implementation, but could demonstrate concrete evidence of how peer interactions contributed to their success, creating a robust framework for understanding not just whether interventions work, but how and why they succeed or fail across different contexts.

    Quantifying learning

    Using a simple methodology that measures learning efficacy across five key variables – scalability, information fidelity, cost effectiveness, feedback quality, and uniformity – we calculated that properly structured peer learning networks achieve an efficacy score of 3.2 out of 4, significantly outperforming both traditional cascade training (1.4) and expert coaching (2.2).

    But the real breakthrough came when considering scale. When calculating the Efficacy-Scale Score (ESS) – which multiplies learning efficacy by the number of learners reached – the differences became stark:

    • Peer Learning: 3,200 (reaching 1,000 learners)
    • Cascade Training: 700 (reaching 500 learners)
    • Expert Coaching: 132 (reaching 60 learners)

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

    The mathematics of scale

    For epidemiologists, the mechanics of this scaling effect may feel familiar.

    In traditional expert-led training, if N is the total number of learners and M is the number of available experts who can each effectively coach K learners, we quickly hit a ceiling where N far exceeds M×K.

    TGLF’s model transforms this equation by structuring interactions so each learner gives and receives feedback from exactly three peers, guided by expert-designed rubrics.

    This creates a linear scaling pattern where total learning interactions = 3N, allowing for theoretically unlimited scale while maintaining quality through structured feedback loops.

    Information loss and network resilience

    One of the most interesting findings concerns information fidelity. In cascade training, knowledge degradation follows a predictable pattern:

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

    where Kn is the knowledge at the nth level of the cascade and α is the loss rate at each step. This explains why cascade training, despite its theoretical appeal, consistently underperforms.

    In contrast, TGLF’s peer learning-to-action networks showed remarkable resilience. By creating multiple pathways for knowledge transmission and building in structured feedback loops, the system maintains high information fidelity even at scale.

    Learn more: Why does cascade training fail?

    References

    Arling, P.A., Doebbeling, B.N., Fox, R.L., 2011. Improving the Implementation of Evidence-Based Practice and Information Systems in Healthcare: A Social Network Approach. International Journal of Healthcare Information Systems and Informatics 6, 37–59. https://doi.org/10.4018/jhisi.2011040104

    Hogan, M.J., Barton, A., Twiner, A., James, C., Ahmed, F., Casebourne, I., Steed, I., Hamilton, P., Shi, S., Zhao, Y., Harney, O.M., Wegerif, R., 2023. Education for collective intelligence. Irish Educational Studies 1–30. https://doi.org/10.1080/03323315.2023.2250309

    Watkins, K.E., Sandmann, L.R., Dailey, C.A., Li, B., Yang, S.-E., Galen, R.S., Sadki, R., 2022. Accelerating problem-solving capacities of sub-national public health professionals: an evaluation of a digital immunization training intervention. BMC Health Serv Res 22, 736. https://doi.org/10.1186/s12913-022-08138-4