Inadequate conflict of interest policies at most French teaching hospitals: A survey and website analysis
Bibliographic record
Abstract
BACKGROUND: There are 32 teaching hospitals in France, including 30 University hospitals and two Regional teaching hospitals. Teaching hospitals have three roles: health care provision, training of healthcare professionals, and medical research. These roles lead to frequent interactions with pharmaceutical and medical device companies, inevitably raising risks of conflicts of interests. Therefore, policies to manage conflict of interests (COI) are crucial. This study aims to examine COI policies in French teaching hospitals. METHODS: All French teaching hospitals (n = 32) were included in this study. All hospitals websites were screened for institutional COI policies and curriculum on COI, using standardized keyword searches. More data were collected through a questionnaire addressed to each chief executive officer (CEO) of the teaching hospital. We used predefined criteria (n = 20) inspired by similar surveys on COI policies in French, US and Canadian medical schools, with some additions to reflect the local hospital context. A global score for each hospital, ranging from 0 to 60 (higher scores denoting stronger policies) was calculated by summing points obtained for each criterion. RESULTS: All 32 hospitals had websites; 21 hospitals listed policies or regulations on their websites or provided them on request. In December 2017, 17 (53.1%) had rules and regulations for some items only, four of which (12.5%) have considered implementing a policy, and only two (6.3%) have begun implementation. 15 (46.9%) had no evidence of COI policies and a null score. The maximum score was 24 out of 60. CONCLUSION: This is the first systematic assessment of COI policies in teaching hospitals in France. Such policies are needed to protect patients, clinicians and students from undue commercial influence. Despite public and political pressure for better management of COI, few teaching hospitals have implemented comprehensive and protective policies, and some hospitals lacked policies altogether. These results highlight the need for greater attention to management of COI within teaching hospitals. One potential solution would be to integrate COI policies into hospital accreditation procedures, in order to ensure a baseline of management at all teaching hospitals.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".