Cannabis harm reduction: perspectives of women who use and allied social and health-care providers
Bibliographic record
Abstract
Background Despite the legalization of cannabis use in Canada in 2018, there remains little research on cannabis harm reduction, particularly for women. Scholarship and public health guidelines tend to focus on the risks of use, emphasizing abstinence rather than harm reduction. Additionally, harm-reduction research and guidelines often lack the perspectives of women who use cannabis and allied social and health-care professionals.Methods This community-based participatory research mixed method study explores the perspectives of women who use cannabis and service providers on the Canadian Lower Risk Cannabis Use Guidelines (LRCUG) and a synthesis of scholarship from 2015 to 2020. The research synthesis and the LRCUG were presented for review by participants in two focus groups (n = 11) and respondents to an online survey (n = 19).Results Participants described public health guidelines as judgmental in tone, ineffective in conveying useful information, and foregrounding abstinence. Participants also identified shortfalls in the research presented, which did not attend to the social context of cannabis use and cannabinoids’ possible benefits alongside risks.Conclusion Participants’ responses affirm that future LRCUGs should focus on informative rather than prescriptive content with meaningful inclusion of people who use cannabis and service professionals as co-creators of knowledge for safer 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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".