Conflict of interest in pharmaceutical policy research: an example from Canada
Bibliographic record
Abstract
Purpose – There is much evidence of bias in research on the effectiveness and efficacy of drugs as a result of the influence of the pharmaceutical industry. The purpose of this paper is to present the views of those involved in a major evidence-based policy initiative from Canada and examine the adequacy of existing academic conflict of interest (COI) rules. Design/methodology/approach – Data came from the Alzheimer’s Drug Therapy Initiative in British Columbia, a coverage with evidence development (CED) initiative, where a form of action research collected insights from the authors’ experiences, combined with qualitative interviews with members of the research team. Findings – The majority of researchers perceive the influence of pharmaceutical manufacturers as problematic. Even when the strictest of COI rules are followed, extending well beyond disclosure, the reach of industry is so great that existing COI rules lag far behind their expanding influence. Practical implications – The authors support others who call for the funding of independent research, enforcement of existing disclosure rules, and unfettered publication rights. In addition, the authors urge the education of all research team members, including clinicians, on the evidence indicating the variety of forms through which industry influence is exerted. The authors believe that this awareness-raising can help toward minimizing that influence in the analyses that are conducted. Originality/value – Consideration of pharmaceutical influence on CED research is important. There may be an untrue assumption that CED is functioning at arms-length from the drug companies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".