A Review of Canadian Medical School Conflict of Interest Policies
Bibliographic record
Abstract
Background:Growing evidence of behavioral bias has caused a surge of interest in the area of Conflict of Interest (COI) within the medical community. The present study sought to evaluate the landscape of Faculty of Medicine COI policies among Canadian medical schools using an evaluation system adapted from the AMSA PharmFree Scorecard.Methods:The authors contacted leaders at the CPD/CME offices of all 17 Canadian medical schools in 2011 to determine how many had formal policies guiding interaction with the pharmaceutical industry. Existing policies were evaluated based on 16 criteria developed by a steering committee. A Policy Score was calculated and a letter grade assigned for each of the existing policies.Results:At the time of review, roughly 35% of the Canadian medical schools had faculty-wide COI policy/guidelines, half of which hadbeen implemented. Other policies are currently in development. Policy Scores ranged from 25.00% to 70.83% with a Mean Policy Score of 52.08%. Policies that were implemented all scored higher than those that were not implemented. Additionally, several strengths and weaknesses among policies were identified.Conclusions:Canadian schools have recognized that COI and bias have becomea serious issue and are taking stepstoward its management. The authors propose that the CMFS employ a system similar to the AMSA Scorecard to evaluate progress in a longitudinal study.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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, 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".