Cumulative Lifetime Violence, Gender, Social Determinants of Health and Mental Health in Canadian Men: A Latent Class Analysis
Bibliographic record
Abstract
Purpose: Among men, violence is pervasive and associated with poor mental health, but little is known about which men are most vulnerable. Our purpose is to address this gap by exploring mental health and social determinants of health (SDOH) including gender role conflict (GRC) in heterogenous groups of men with distinct patterns of cumulative lifetime violence (CLV) as target and perpetrator. Methods: Latent class analysis was conducted using means of 64 indicators of CLV severity collected from a community sample of 685 eastern Canadian men, ages 19 to 65 years. Class differences by SDOH, and depression, anxiety, and posttraumatic stress disorder (PTSD) were explored with Chi-square and analysis of variance. Results: A 4-class solution was optimal. Class 1 had the lowest CLV severity; were more likely to be better educated, employed, and have little difficulty living on their incomes; and had better mental health than other classes. Class 2, characterized by moderate psychological violence as both target and perpetrator, had mean depression and PTSD scores at clinical levels, and more difficulty living on income than Class 1. Classes 3 and 4 were typified by high severity CLV as target but differentiated by Class 4 having the highest perpetration severity, higher GRC, and being older. In both classes, mean mental health scores were above cut-offs for clinical symptomology and higher than Classes 1 and 2. Conclusion: This is the first evidence that distinct patterns of CLV severity among men intersect with GRC and SDOH and are uniquely associated with mental health.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".