0849 A Cross-Sectional Survey of Cannabis as a Sleep Aid in Canadian Cancer Survivors
Bibliographic record
Abstract
Abstract Introduction Cannabis is increasingly used to manage cancer treatment-related symptoms. This study investigated the use and perceived effects of cannabis as a sleep aid in Canadian cancer survivors. Methods Canadian cancer survivors (N=1492) completed a self-report questionnaire about their use of cannabis as a sleep aid. Results Of participants, 24% (n=356) reported currently using cannabis as a sleep aid (Mage = 61; 88% white, 78% in remission). Two thirds (67%; n=238) report only starting to use cannabis for sleep after their cancer diagnosis. Reported benefits include that cannabis helps them relax (59%) and fall asleep faster (48%). Half of participants (49%) use cannabis for sleep over 4-6 times a week, meeting the criteria for regular cannabis use and 53% have been using cannabis as a sleep aid for more than 2 years. Cannabis is most commonly used in the form of edibles (58%) and oils/ sprays (45%). Only 19% have authorization from a healthcare provider and 56% obtain their cannabis from a government regulated site. There was a mix of cannabinoid content in the products used, including products that contain mostly CBD (22%), mostly THC (35%), balanced amounts of CBD and THC (32%), and 8% do not know the formulation of the cannabis they consume. The most common reasons for trying cannabis for sleep were a recommendation from family/friends (43%), their own research (35%), and that they believed it to be more natural than medication (31%). Of those who use cannabis for sleep, 31% also use it to reduce pain and 23% use it recreationally. Conclusion Estimates suggest that one in four Canadian cancer survivors’ regularly use cannabis to improve their sleep. Given the prevalence and potential impact, more research is needed to examine the actual efficacy of cannabis as a sleep aid. Support (if any) Rachel Lee is a trainee in the Cancer Research Training Program of the Beatrice Hunter Cancer Research Institute, with funds generously provided by the Canadian Cancer Society’s JD Irving, Limited – Excellence in Cancer Research Fund. Dr Garland is supported by a Canadian Cancer Society Emerging Scholar Award (Survivorship) (grant #707146).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".