Evidence map of knowledge translation strategies, outcomes, facilitators and barriers in African health systems
Bibliographic record
Abstract
BACKGROUND: The need for research-based knowledge to inform health policy formulation and implementation is a chronic global concern impacting health systems functioning and impeding the provision of quality healthcare for all. This paper provides a systematic overview of the literature on knowledge translation (KT) strategies employed by health system researchers and policy-makers in African countries. METHODS: Evidence mapping methodology was adapted from the social and health sciences literature and used to generate a schema of KT strategies, outcomes, facilitators and barriers. Four reference databases were searched using defined criteria. Studies were screened and a searchable database containing 62 eligible studies was compiled using Microsoft Access. Frequency and thematic analysis were used to report study characteristics and to establish the final evidence map. Focus was placed on KT in policy formulation processes in order to better manage the diversity of available literature. RESULTS: The KT literature in African countries is widely distributed, problematically diverse and growing. Significant disparities exist between reports on KT in different countries, and there are many settings without published evidence of local KT characteristics. Commonly reported KT strategies include policy briefs, capacity-building workshops and policy dialogues. Barriers affecting researchers and policy-makers include insufficient skills and capacity to conduct KT activities, time constraints and a lack of resources. Availability of quality locally relevant research was the most reported facilitator. Limited KT outcomes reflect persisting difficulties in outcome identification and reporting. CONCLUSION: This study has identified substantial geographical gaps in knowledge and evidenced the need to boost local research capacities on KT practices in low- and middle-income countries. Evidence mapping is also shown to be a useful approach that can assist local decision-making to enhance KT in policy and practice.
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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 | 0.101 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.037 | 0.043 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".