Declarations of interest by members of Health Canada’s special advisory committees and panels: a descriptive study
Bibliographic record
Abstract
Background: Health Canada supplements its in-house expertise on pharmacotherapy and pharmaceutical policy through the use of scientific/expert advisory committees and scientific/expert advisory panels. This study was undertaken to examine the interests of the members of these Health Canada advisory bodies. Methods: This was an observational study of the financial and intellectual interests of members of Health Canada’s scientific/expert advisory committees and panels. The following information was extracted from Health Canada websites in December 2018: member’s name, name of committee/panel, direct and indirect financial interests, and intellectual interests. Information extracted about the committees and panels included the number of meetings for which a record of proceedings was available and the topics discussed at the meetings. Results: Of 81 unique committee and panel members, 12 declared a direct financial interest, 56 an indirect financial interest and 65 an intellectual interest. Five of 11 committees and panels had people who declared a direct financial interest. All 11 advisory bodies had members who declared indirect financial interests (n = 62) and intellectual interests (n = 81). Six of the 11 committees and panels had a majority of members who declared a direct or indirect financial interest. In the 10 advisory body meetings for which information was available, individual products were rarely discussed but recommendations from all but 1 of the meetings could potentially have affected sales. Interpretation: Only a minority of members of Health Canada’s advisory committees and panels declared direct financial interests but the majority of members of a majority of the advisory bodies declared indirect financial and intellectual interests. Because of the lack of individual voting records it was not possible to determine if financial or intellectual interests influenced voting patterns.
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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 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".