Patterns and correlates of cannabis use by cumulative lifetime violence severity as target and/or perpetrator in a community sample of eastern Canadian men
Bibliographic record
Abstract
BACKGROUND: Recent Canadian legalization of cannabis for non-medical use underscores the need to understand patterns and correlates of cannabis use among men who may be more likely than women to become problematic cannabis users. Evidence supporting an association between substance use and violence is accumulating. Current knowledge of relationships among patterns of cannabis use, violence, gender and health is limited by dichotomous measurement of cannabis use and a focus on individual types of violence rather than lifetime cumulative violence. METHODS: We collected online survey data between April 2016 and Septermber 2017 from a community convenience sample of 589 Eastern Canadian men ages 19 to 65 years and explored how socio-demographic characteristics, gender, and health varied by past-year patterns of cannabis use (i.e., daily, sometimes, never) in the total sample and by higher and lower cumulative lifetime violence severity (CLVS) measured by a 64-item CLVS scale score (1 to 4). RESULTS: (2) = 31.53, p < .001). In the total sample, daily use was significantly associated with being single, less education, lower income, some gender norms, health problems, and use of other substances. Significant associations were found for sometimes cannabis use with age group 19 to 24 years, being single, some gender norms, and hazardous and binge drinking. Never use was associated with being married, more education, higher income, being older, not using other substances, and not having mental health problems. Associations between cannabis use patterns and many variables were found in both CLVS groups but effect sizes were frequently larger in the higher group. CONCLUSIONS: These results add substantively to knowledge of relationships among lifetime cumulative violence, patterns of cannabis use, gender, socio-demographic indicators and health problems and may inform theoretical models for future testing. Additionally, findings provide critical information for the design of health promotion strategies targeted towards those most at risk in the current climate of cannabis legalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".