Cumulative lifetime violence, social determinants of health, and cannabis use disorder post‐cannabis legalization in a community sample of men: An intersectional perspective
Bibliographic record
Abstract
Despite Canada having the highest disease burden globally for cannabis use disorder (CUD) and violence being ubiquitous in men's lives, little is known about how intersections among social determinants of health (SDOH) and cumulative lifetime violence severity (CLVS) influence CUD in men post-cannabis legalization. Using data collected in a survey with a national community sample of 597 men who self-identified as having experienced violence, we conducted a latent profile analysis using 11 subscales of the CLVS-44 scale and explored differential associations between CLVS profiles and CUD considering SDOH covariates. Four profiles were distinguished by intersections among CLVS-44 subscale severity and roles as target and perpetrator. CLVS profiles were significantly associated with CUD in the unadjusted model and in the adjusted model where age, adverse housing, and education were significant covariate controls. In the adjusted model, CUD was differentially associated with CLVS profiles and significantly higher in Profile 4 (highest severity target and perpetrator) than in Profile 1 (lowest severity target, no perpetration). Chi-square tests showed significant intersection between adverse housing, younger age, Profile 4 CLVS, and moderate to severe CUD among cannabis users. These results reveal the importance of understanding simultaneous intersections among indicators of CLVS in determining profiles of lifetime violence. Also critical are intersections among CLVS profiles and significant covariates as a basis for trauma- and violence-informed care for CUD that prioritizes men most disadvantaged by this convergence and attends to individual and structural health disparities at practice and policy levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".