Cannabis companies and the sponsorship of scientific research: A cross-sectional Canadian case study
Bibliographic record
Abstract
Corporations across sectors engage in the conduct, sponsorship, and dissemination of scientific research. Industry sponsorship of research, however, is associated with research agendas, outcomes, and conclusions that are favourable to the sponsor. The legalization of cannabis in Canada provides a useful case study to understand the nature and extent of the nascent cannabis industry's involvement in the production of scientific evidence as well as broader impacts on equity-oriented research agendas. We conducted a cross-sectional, descriptive, meta-research study to describe the characteristics of research that reports funding from, or author conflicts of interest with, Canadian cannabis companies. From May to August 2021, we sampled licensed, prominent Canadian cannabis companies, identified their subsidiaries, and searched each company name in the PubMed conflict of interest statement search interface. Authors of included articles disclosed research support from, or conflicts of interest with, Canadian cannabis companies. We included 156 articles: 82% included at least one author with a conflict of interest and 1/3 reported study support from a Canadian cannabis company. More than half of the sampled articles were not cannabis focused, however, a cannabis company was listed amongst other biomedical companies in the author disclosure statement. For articles with a cannabis focus, prevalent topics included cannabis as a treatment for a range of conditions (15/72, 21%), particularly chronic pain (6/72, 8%); as a tool in harm reduction related to other substance use (10/72, 14%); product safety (10/72, 14%); and preclinical animal studies (6/72, 8%). Demographics were underreported in empirical studies with human participants, but most included adults (76/84, 90%) and, where reported, predominantly white (32/39, 82%) and male (49/83, 59%) participants. The cannabis company-funded studies included people who used drugs (37%) and people prescribed medical cannabis (22%). Canadian cannabis companies may be analogous to peer industries such as pharmaceuticals, alcohol, tobacco, and food in the following three ways: sponsoring research related to product development, expanding indications of use, and supporting key opinion leaders. Given the recent legalization of cannabis in Canada, there is ample opportunity to create a policy climate that can mitigate the harms of criminalization as well as impacts of the "funding effect" on research integrity, research agendas, and the evidence base available for decision-making, while promoting high-priority and equity-oriented independent 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 | 0.029 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.032 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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 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".