Canadian cannabis researcher perspectives on the conduct and sponsorship of scientific research by the for-profit cannabis industry
Bibliographic record
Abstract
There has been considerable financial investment by the for-profit cannabis industry to conduct research on cannabis in Canada. Similar to peer industry counterparts such as the pharmaceutical, alcohol, tobacco, and food industries, there is evidence that for-profit cannabis companies are financially sponsoring research programs and researchers as well as non-financially, such as donating products. However, a large body of research has established that researchers' financial relationships with industries may influence research agendas, outcomes, lead to conflicts of interest, and bias the evidence base. Within a complex, emerging context of legalization, there is limited information on how cannabis researchers negotiate their relationships with the for-profit cannabis industry in Canada. Following a qualitative phenomenological methodology informed by moral experience for bioethics research, we conducted 38 semi-structured interviews with academic researchers, peer researchers, and clinicians with relevant perspectives about Canadian cannabis companies' research activities. We used a codebook approach to thematic analysis which generated three central themes: Navigating Systemic Barriers to Conduct Research; Impressions and Influences; and Guiding Principles for an Ethical Research Process. Our findings suggest that Canadian cannabis researchers tend to be morally ambivalent about cannabis industry sponsorship of research: they are motivated to conduct high quality research and generate evidence for population health benefit, yet they have concerns over the potential for research agenda bias created by these relationships which could be harmful to population health. Participants spoke how they relied heavily on personal values and individual strategies (transparency, value alignment, arms-length association, independence) to determine how they manage cannabis industry relationships. Our findings highlight how the issue of industry-academic relationships is a structural problem, thus individual-level solutions without attention to the relationship itself will only deepen ethical worries about industry-sponsored research.
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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 | 0.057 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.060 | 0.048 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| 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".