Conflict of Interest Policies at French Medical Schools: Starting from the Bottom
Bibliographic record
Abstract
BACKGROUND: Medical faculties have a role in ensuring that their students are protected from undue commercial influence during their training, and are educated about professional-industry interactions. In North America, many medical faculties have introduced more stringent conflict of interest (COI) policies during the last decade. We asked whether similar steps had been taken in France. We hypothesized that such policies may have been introduced following a 2009-2010 drug safety scandal (benfluorex, Mediator) in which COIs in medicine received prominent press attention. METHODS: We searched the websites of all 37 French Faculties of Medicine in May 2015 for COI policies and curriculum, using standardized keyword searches. We also surveyed all deans of medicine on institutional COI policies and curriculum, based on criteria developed in similar US and Canadian surveys. Personal contacts were also consulted. We calculated a summary score per faculty based on 13 criteria. [range 0-26; higher scores denoting stronger policies]. RESULTS: In total, we found that 9/37 (24%) of French medical schools had either introduced related curriculum or implemented a COI-related policy. Of these, only 1 (2.5%) had restrictive policies for any category. No official COI policies were found at any of the schools. However, at 2 (5%), informal policies were reported. The maximum score per faculty was 5/26, with 28 (76%) scoring 0. CONCLUSION: This is the first survey in France to examine COI policies at medical faculties. We found little evidence that protection of medical students from undue commercial influence is a priority, either through institutional policies or education. This is despite national transparency legislation on industry financing of health professionals and limits on gifts. The French National Medical Students Association (ANEMF) has called for more attention to COI in medical education; our results strongly support such a call.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".