Research use at the Ministry of Health in Burkina Faso: the decision-makers’ perspective
Bibliographic record
Abstract
BACKGROUND: Despite the increased emphasis placed on the use of evidence for policy development, relatively few initiatives have been developed to support evidence-informed decision-making, especially in West Africa. Moreover, studies examining the conditions under which policy-makers use research-based evidence are still scarce, but they show that their attitudes and opinions about research are one of the main determinants of such use. In February 2017, Burkina Faso's Minister of Health planned to create a unit to promote evidence-informed decision-making within the ministry. Before the unit was set up, documenting the attitudes towards research at the highest levels of his Ministry appeared profitable to the unit's planning. METHOD: Individual interviews were conducted by the author with 14 actors positioned to consider evidence during decision-making from the Burkina Faso's Minister of health cabinet. An interview grid was used to explore several themes such as attitudes towards research, obstacles and facilitators to research use, example of research use in decision-making and finally, ways to increase decision-makers' participation in knowledge transfer activities. Interviews were partially transcribed and analysed by the author. RESULTS: The results show a mixed attitude towards research and relatively little indication of research use reported by respondents. Important obstacles were identified: evidence inaccessibility, lack of implementation guidelines, absence of clear communication strategy and studies' lack of relevance for decision-making. Many suggestions were proposed such as raising awareness, improving access and research communication and prioritizing interactions with researchers. Respondents agree with the low participation of decision-makers in knowledge transfer activities: more leadership from the senior officials was suggested and greater awareness of the importance of their presence. CONCLUSIONS: The conclusion presents avenues for reflection and action to increase the potential impact of the knowledge transfer unit planned within the Ministry of Health of Burkina Faso. This innovative initiative will be impactful if the obstacles identified in this study and policy-makers' preferences and needs are taken into account during its development and implementation.
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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 | 0.097 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 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".