A deliberative dialogue as a knowledge translation strategy on road traffic injuries in Burkina Faso: a mixed-method evaluation
Bibliographic record
Abstract
INTRODUCTION: Deliberative dialogues are increasingly being used, particularly on the African continent. They are a promising interactive knowledge translation strategy that brings together and leverages the knowledge of diverse stakeholders important to the resolution of a societal issue. Following a research project carried out in Burkina Faso on road traffic injuries, a 1-day workshop in the form of a deliberative dialogue was organised in November 2015. The workshop brought together actors involved in road safety, such as researchers, police and fire brigades, health professionals, non-governmental and civil society organisations, and representatives of government structures. The objective was to present the research results, propose recommendations to improve the situation and develop a collective action plan. METHOD: To better understand the workshop's utility and effects, a mixed-method evaluation was conducted. Data were obtained from two questionnaires distributed at the end of the workshop (n = 37) and 14 qualitative interviews with participants 6-10 weeks after the workshop. Descriptive statistics were used to analyse the quantitative data, and a thematic analysis was conducted for the qualitative data. RESULTS: The data revealed several positive impacts of the workshop, such as the acquisition of new knowledge about road safety, the opportunity for participants to learn from each other, the creation of post-workshop collaborations, and individual behaviour changes. However, several challenges were encountered that constrained the potential effects of the workshop, including the limited presence of political actors, the lack of engagement among participants to develop an action plan, and the difficulty in setting up a monitoring committee following the workshop. CONCLUSION: While the deliberative workshop is not the standard format for reporting research results in Burkina Faso, this model should be reproduced in different contexts. This interactive knowledge translation strategy is useful to benefit from the experiential knowledge of the various actors and to encourage their involvement in formulating recommendations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.246 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".