Perceptions of cannabis as a stigmatized medicine: a qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: Despite its increasing prevalence and acceptance among the general public, cannabis use continues to be viewed as an aberrant activity in many contexts. However, little is known about how stigma associated with cannabis use affects individuals who use cannabis for therapeutic purposes (CTP) and what strategies these individuals employ to manage associated stigma. The aim of this Canadian study was to describe users' perceptions of and responses to the stigma attached to using CTP. METHODS: Twenty-three individuals who were using CTP for a range of health problems took part in semi-structured interviews. Transcribed data were analyzed using an inductive approach and comparative strategies to explore participants' perceptions of CTP and identify themes. RESULTS: Participant experiences of stigma were related to negative views of cannabis as a recreational drug, the current criminal sanctions associated with cannabis use, and using cannabis in the context of stigmatizing vulnerability (related to existing illness and disability). Strategies for managing the resulting stigma of using CTP included: keeping CTP 'undercover'; educating those who did not approve of or understand CTP use; and using cannabis responsibly. CONCLUSIONS: Understanding how individuals perceive and respond to stigma can inform the development of strategies aimed at reducing stigma associated with the use of CTP and thereby address barriers faced by those using this medicine.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".