Gender influences on cannabis use among treatment-seeking adults: a qualitative study
Bibliographic record
Abstract
Background Gender differences in cannabis use, related harms, and in the development and progression of cannabis use disorder have been reported. Better understanding and identifying why these differences exist is important in developing and tailoring prevention and treatment approaches.Methods This qualitative interview study explored influences of gender on cannabis use trajectories. Twenty-three adults (10 cisgender women, 12 cisgender men, 1 non-binary person) who had received treatment for cannabis-related problems were asked about the relationship between their gender and cannabis experiences.Results Reflexive thematic analysis was used to develop five themes. First, many participants seemingly did not perceive a strong relationship between their gender and cannabis use. Second, men’s cannabis use was impacted by masculinity facilitating the initiation and escalation of use, while simultaneously acting as a barrier to seeking treatment. Third, men’s motivations for using cannabis were almost always reported to be recreational, which is likely linked to men’s use being more normative. Fourth, almost all women reportedly used cannabis to cope, especially with mental health problems. Fifth, social relationships and gender dynamics constrained women’s cannabis use, particularly where many felt pressured into seeking treatment due to increased stigma for women who use cannabis.Conclusions Our findings revealed gender differences in cannabis use trajectories, including treatment seeking, barriers, and facilitators, emphasizing the importance of developing gender-specific approaches for reducing cannabis-related harms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".