Cannabidiol use among elite-level Canadian athletes: the pursuit of improved sleep, pain relief, and enhanced recovery
Bibliographic record
Abstract
Introduction Cannabidiol (CBD) is a compound in the cannabis plant with psycho-physiological effects that may support athletes’ training and recovery. Although not banned by the World Anti-Doping Agency, CBD products may carry a risk of inadvertent anti-doping violations due to contamination with prohibited cannabinoids. The primary objective of this study was to characterize the use, rationale, and perceived benefits of CBD use by elite-level athletes in Canada. The secondary objectives were to (1) identify the sources of information that influence CBD use, (2) describe how athletes are using CBD, and (3) explore the barriers or deterrents to its use. Design Cross-sectional descriptive survey study. Methods Elite-level Canadian athletes completed an anonymous online survey on CBD use between October 2021–June 2023. Results 80 athletes completed the survey. 38% ( n = 30) had used CBD, with 30% ( n = 9) of CBD users reporting active/current use. CBD users cumulatively agreed or strongly agreed that CBD is safe (96%); improved sleep (93%) and relaxation (90%); and reduced pain from training (77%). Friends (26%) and the internet (24%) were the most frequently reported first sources of information on CBD. Oral tincture/oil was the most commonly used (31%) form of CBD. The most reported reason for never using or discontinuing CBD was concern about an anti-doping rule violation (28%). Discussion Given the self-reported benefits of CBD among elite-level Canadian athletes, alongside concerns about inadvertent anti-doping violations, clinicians working with this population should provide evidence-based guidance on CBD use and support informed decision-making to minimize risk and optimize athlete safety.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".