How older Canadians access medical cannabis and information about it: A descriptive survey
Bibliographic record
Abstract
To understand older adults’ perceptions regarding access to medical cannabis and information related to it. We employed cross-sectional survey design to recruit Canadians (≥60 years) who consume cannabis for health purpose(s). Recruitment flyers were circulated to potential sites (e.g. CanAge). Interested participants self-selected to participate by contacting the researcher and completed the online survey. Data were analyzed using descriptive statistics and conventional content analysis. A total of 107 older adults participated in the study. Most sought information from healthcare professionals (HCP) (49.5%) or cannabis retail stores (40.2%) and accessed cannabis via retail stores (56.1%). High cost, judgement from others, and HCP reluctance to prescribe were reported as the common barriers to access. Participants most often sought information related to benefits, safety, and dosage of medical cannabis. Many older adults relied on medically unauthorized sources such as retail stores to access medical cannabis and information related to it. At the policy level, initiatives are needed to help older adults for whom medical cannabis may be appropriate to learn about and access medical cannabis via authorized sources. To ensure safe and effective medical cannabis use, knowledge products about medical cannabis are necessary for older adults.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".