A Knowledge Brokering Program in Burkina Faso (West Africa): Reflections from Our Experience
Bibliographic record
Abstract
-In Burkina Faso, inadequate interaction among researchers, decision makers, and practitioners, together with low use of research results, impedes the development of health policies and interventions to improve equity. A knowledge translation strategy was implemented as part of a research program. The broker and his team promoted links between actors (health agents, nongovernmental organizations, public administration, policy makers, researchers), provided them with research results related to their needs, and supported them in applying this knowledge in their practices. The strategy was first implemented in Kaya District, Burkina Faso. To increase impact on population health, the strategy included widening the sphere of action through collaboration with the Ministry of Health. The broker was affiliated with a public health consulting firm in the capital, Ouagadougou, and supported by Canadian experts and a senior Burkinabè broker. Evaluation shows that research use increased at the local level among health mutuals, regional nongovernmental organizations, and health professionals in Kaya, but the objective of reaching Ministry of Health decision makers was not achieved. Results highlight the need for better training in knowledge transfer for both local and international researchers and proper identification of the gateways to reach high level decision makers. This ambitious strategy encountered several obstacles: difficult access to decision makers, poor team communication, and broker's nonconducive working environment. Future brokering strategies should analyze the political situation in depth to determine when and how to approach national and regional decision makers; invest time and effort in developing different actors' (including researchers') knowledge transfer skills; and ensure sufficient and good quality communications and resources within the team.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".