Les conflits d’intérêts chez les médecins : un bon exemple de l’apport du Code de déontologie des médecins afin d’assurer la protection du public
Bibliographic record
Abstract
Nous nous intéressons dans le cadre de ce travail aux situations de conflits d’intérêts chez les médecins. Nous discutons dans un premier temps des bases du système professionnel québécois. Nous traitons par la suite de l’obligation pour les ordres professionnels de se doter d’un code de déontologie, règlement qui doit notamment prévoir des dispositions visant à prévenir les situations de conflits d’intérêts. Nous définissons ensuite ce que constitue un conflit d’intérêts et nous étudions un à un les articles situés dans la section « Indépendance et désintéressement » du Code de déontologie des médecins afin d’expliquer l’étendue des obligations des médecins québécois.
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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 | 0.026 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.024 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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".