Collaborative research and knowledge translation on road crashes in Burkina Faso: the police perspective 18 months on
Bibliographic record
Abstract
In this commentary, we present a follow-up of two articles published in 2017 and 2018 about road traffic crashes, which is an important public health issue in Africa and Burkina Faso. The first article reported on a research project, conducted in partnership with local actors involved in road safety, carried out in Ouagadougou in 2015. Its aim was to test the effectiveness, acceptability, and capacity of a surveillance system to assess the number of road traffic crashes and their consequences on the health of crash victims. Several knowledge translation activities were carried out to maximize its impact and were reported in the 2018 article published in HRPS: monthly reports presenting the research data, large-format printed maps distributed to the city's police stations, and a deliberative workshop held at the end of the research project. The present commentary presents our efforts to deepen our understanding of the impacts of the knowledge translation strategy, based on follow-up interviews, 18 months after the workshop, with the heads of the road traffic crash units in Ouagadougou police stations (n = 5). Several benefits were reported by respondents. Their involvement in the process prompted them to broaden their knowledge of other ways of dealing with the issue of road crashes. This led them, sometimes with their colleagues, to intervene differently: more rapid response at collision sites, increased surveillance of dangerous intersections, user awareness-raising on the importance of the highway code, etc. However, sustaining these actions over the longer term has proven difficult. Several lessons were derived from this experience, regarding the importance of producing useful and locally applicable research data, of ensuring the acceptability of the technologies used for data collection, of using collaborative approaches in research and knowledge translation, of ensuring the visibility of actions undertaken by actors in the field, and of involving decision-makers in the research process to maximize its impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".