Understanding cancer survivors’ reasons to medicate with cannabis: A qualitative study based on the theory of planned behavior
Bibliographic record
Abstract
BACKGROUND: Prior to nonmedical cannabis legalization in Canada, individuals were only able to access cannabis legally through licensed producers with medical authorization. Now with an additional legal access system designed for nonmedical purposes, it is unclear what factors influence cancer survivors' decisions to medicate or not medicate cannabis as a complementary therapy to alleviate their cancer symptoms. METHODS: We recruited cancer survivors via social media. Interested individuals were purposively sampled to ensure maximization in terms of age, sex, and province of residence. Constructs of the Theory of Planned Behavior were explored during the telephone interviews as participants described what influenced their decisions to medicate or not medicate cannabis to manage their symptoms. RESULTS: Interviews were conducted with 33 cancer survivors. All individuals believed that cannabis would manage their cancer symptoms. Those that chose to medicate with cannabis provided a variety of reasons, including that cannabis was a more natural alternative; that it reduced their overall number of prescription drugs; and that safer products had become available with the legalization of nonmedical cannabis. Some individuals also indicated that support from physicians and validation from family and friends were important in their decision to medicate with cannabis. Individuals who opted not to medicate with cannabis raised concerns about the lack of scientific evidence and/or possible dependency issues. Some also felt their physician's disapproval was a barrier to considering cannabis use. CONCLUSIONS: The findings revealed that recreational legalization made using cannabis appear safer and easier to access for some cancer survivors. However, physicians' censure of cannabis use for symptom management was a barrier for survivors considering its use.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".