Collaborative development and implementation of a knowledge brokering program to promote research use in Burkina Faso, West Africa
Bibliographic record
Abstract
Despite efforts expended over recent decades, there is a persistent gap between the production of scientific evidence and its use. This is mainly due to the difficulty of bringing such knowledge to health workers and decision-makers so that it can inform practices and decisions on a timely basis. One strategy for transferring knowledge to potential users, that is, gaining increasing legitimacy, is knowledge brokering (KB), effectiveness of which in certain conditions has been demonstrated through empirical research. However, little is known about how to implement such a strategy, especially in the African context. The KB program presented here is aimed specifically at narrowing the gap by making scientific knowledge available to users with the potential to improve health-related practices and decision making in Burkina Faso. The program involves Canadian and African researchers, a knowledge broker, health practitioners, and policy-makers. This article presents the collaborative development of the KB strategy and the evaluation of its implementation at year 1. The KB strategy was developed in stages, beginning with a scoping study to ensure the most recent studies were considered. Two one-day workshops were then conducted to explore the problem of low research use and to adapt the strategy to the Burkinabè context. Based on these workshops, the KB program was developed and brokers were recruited and trained. Evaluation of the program's implementation after the first year showed that: 1) the preparatory activities were greatly appreciated by participants, and most considered the content useful for their work; 2) the broker had carried out his role in accordance with the logic model; and 3) this role was seen as important by the participants targeted by the activities and outputs. Participants made suggestions for program improvements in subsequent years, stressing particularly the need to involve decision-makers at the central level.
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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 | 0.086 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| 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".