The experience of a regional network of health policy and systems actors in translating evidence into policy and action in West Africa
Bibliographic record
Abstract
Objectives: To identify strategies and interventions to strengthen the generation and use of research evidence in health policy and practice decision-making and implementation in the West African sub-region (knowledge translation).Design: The study design was cross-sectional. Data sources were from a desk review, West African Network of Emerging Leaders (WANEL) member brainstorming, and group discussion outputs from WANEL members and session participants’ discussions and reflections during an organised session at the 2019 African Health Economics and Policy Association meeting in Accra.Results: Strategies and interventions identified included developing a Community of Practice, a repository of health policy and systems research (HPSR) evidence, stakeholder mapping, and engagement for action, advocacy, and partnership. Approaches for improving evidence uptake beyond traditional knowledge translation activities included the use of cultural considerations in presenting research results and mentoring younger people, the presentation of results in the form of solutions to political problems for decision-makers, and the use of research results as advocacy tools by civil society organisations. Development of skills in stakeholder mapping, advocacy, effective presentation of research results, leadership skills, networking, and network analysis for researchers was also identified as important.Conclusions: To strengthen the generation and use of research evidence in health policy and practice decision-making in West Africa requires capacity building and multiple interventions targeted synergistically at researchers, decision-makers, and practitioners.
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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 | 0.044 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| 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".