Implementation science in Africa—whose epistemology counts?
Bibliographic record
Abstract
Implementation science, although promising to bridge the know-do gap in global health, has inadvertently created new forms of epistemic exclusion in African health systems. In this Viewpoint, we present an empirical critique of how widely used implementation frameworks, rooted in Eurocentric and North American epistemologies, systematically fail to recognise the mechanisms through which successful implementation occurs in African contexts. Drawing on case studies across diverse African settings, we reveal how this epistemological mismatch undermines both the science and practice of implementation in African health systems. Using epistemic injustice theory, we show how frameworks operationalise constructs in ways that treat traditional governance, community legitimacy, and relational authority as peripheral variables rather than generative mechanisms of change. We propose concrete transformations to implementation science that centre African epistemological traditions and require genuine power-sharing in knowledge production to support health system improvement across all contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.093 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".