A Cross Sectional Survey of Factors Related to Cannabis Use as a Sleep Aid Among Canadian Cancer Survivors
Bibliographic record
Abstract
OBJECTIVES: Poor sleep is a common side effect of cancer. Cannabis is increasingly used to manage cancer treatment-related symptoms, including sleep. This study investigated factors related to cannabis use for sleep among Canadian cancer survivors. METHOD: = 940) were recruited via the Angus Reid Institute and completed an online, cross-sectional survey. Univariate and multiple binomial logistic regression models identified factors associated with cannabis use for sleep. RESULTS: = 236) currently use cannabis for sleep. Participants were at greater odds of using cannabis for sleep if they identified as a gender other than man or woman (AOR = 11.132), were diagnosed with multiple medical conditions (2:AOR = 1.988; 3+:AOR = 1.902), two psychological conditions (AOR = 2.171), multiple sleep disorders (AOR = 2.338), insomnia (AOR = 1.942), bone (AOR = 6.535), gastrointestinal (AOR = 4.307), genitourinary (AOR = 2.586), hematological (AOR = 4.739), or an unlisted cancer (AOR = 3.470), received hormone therapy only (AOR = 3.054), drink heavily (AOR = 2.748), or had mild insomnia (AOR = 1.828). Older participants (AOR=.972) and those with sleep apnea were less likely to use cannabis for sleep (AOR=.560). CONCLUSION: Given its prevalence, research is needed to understand how factors associated with cannabis use as a sleep aid among Canadian cancer survivors may influence its use and effectiveness and whether these factors are barriers to accessing evidence-based treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".