Continuing medical education and pharmaceutical industry involvement: An evaluation of policies adopted by Canadian professional medical associations
Bibliographic record
Abstract
BACKGROUND: Professional medical associations (PMAs) play a crucial role in providing accredited continuing medical education (CME) to physicians. Funding from the pharmaceutical industry may lead to biases in CME. OBJECTIVE: This study examines publicly available policies on CME, adopted by Canadian PMAs as of December 2015. METHODS: Policies were evaluated using an original scoring tool comprising 21 items, two questions about PMAs' general and CME funding from industry, and three enforcement measures. RESULTS: We assessed 236 policies adopted by Canadian PMAs (range, 0 to 32). Medical associations received summative scores that ranged from 0% to 49.2% of the total possible points (maximum score = 63). Twenty-seven associations received an overall score of 0%. The highest mean scores were achieved in the areas of industry involvement in planning CME activities (mean: 1.1/3), presence of a review process for topics of CME activities (mean: 1.1/3), content review for balanced information (mean: 1.1/3), and responsibility of distribution of funds (mean: 1.0/3). The lowest mean scores were achieved in the areas of awards (mean: 0.0/3), industry personnel, representatives, and employees (mean: 0.1/3), distribution of industry-funded educational materials at CME activities (mean: 0.1/3), and distinction between marketing and educational materials (mean: 0.1/3). CONCLUSION: These results suggest that Canadian PMAs' publicly available policies on industry involvement in CME are generally weak or non-existent; therefore, the accredited CME that is provided to Canadian physicians may be viewed as open to bias. We encourage all Canadian medical associations to strengthen their policies to avoid the potential for industry influence in CME.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".