Améliorer la compréhension et la gestion des conflits d’intérêts des experts conseillant la prise de décisions en santé publique
Bibliographic record
Abstract
Au Québec, au Canada et ailleurs dans le monde, des comités consultatifs d’experts conseillent et orientent les décideurs gouvernementaux dans le choix de nouveaux médicaments, de vaccins à utiliser ou encore d’interventions à mettre en place. Parallèlement, ces experts bénéficient d’un appui de plus en plus important d’entreprises privées pour réaliser leurs recherches ou en diffuser les résultats. Cette situation les met à risque de conflits d’intérêts et peut, éventuellement, miner la confiance de la population envers la prise de décision publique. Cette étude de cas suscite des réflexions pertinentes quant à ce qui constitue une gestion saine et optimale des situations de conflits d’intérêts par les membres experts et les organisations dans lesquelles ils ont un rôle-conseil.
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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 | 0.117 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".