Perceived support for medical cannabis use among approved medical cannabis users in Canada
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Very little is known about the social experience of medical cannabis use, including the experience of stigma among approved users. The current study examined perceptions of support from physicians, family and friends as well as the prevalence of 'hiding' medicinal cannabis use. DESIGN AND METHODS: An online cross-sectional survey (N = 276) was conducted from 29 April to 8 June 2015. No public sampling frame was available from which to sample approved medical cannabis users (MCU). Eligible respondents were approved MCUs, aged 18 years or older, and reported cannabis use in the past 30 days for health reasons. Logistic regression analyses were used to assess aspects of stigma, including perceived support from their immediate social environment as well as behaviours reflecting a perceived social disapproval. RESULTS: Approximately one-third of respondents (32.6%) reported that their physician had refused to provide a medical document, and the vast majority of respondents (79.3%) reported hiding their medical cannabis use, most commonly to avoid judgement. Fewer than half of approved users perceived that their doctor was 'supportive' (38%), whereas two-thirds perceived support from family (66.3%) and friends (66.3%). Perceptions of support were similar across most socio-demographic sub-groups. DISCUSSION AND CONCLUSIONS: Substantial proportions of approved MCUs in Canada report a lack of support and most have made some effort to conceal their medical cannabis use. Overall, the findings suggest that social norms around medical cannabis use remain unfavourable for many users, despite that fact that medical cannabis has been legal in Canada for more than a decade.
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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 | 0.004 |
| 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 | 0.000 | 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".