Do Injury, Depression, and PTSD Mediate the Relationships Between Latent Profiles of Cumulative Lifetime Violence and Chronic Pain Disability in Men?
Bibliographic record
Abstract
Knowledge of association between violence and chronic pain in men is limited by neglect of violence experiences as perpetrator, disregard of heterogeneity in cumulative lifetime violence severity (CLVS), weak understanding of mediation pathways, and inattention to social determinants of health (SDOH). The CLVS-44 measure and identification of four distinct latent profiles of CLVS facilitated addressing these shortcomings. CLVS-44 data from a national cross-sectional community survey of 587 Canadian men who had violence experiences as target and/or perpetrator were used in parallel multiple mediation analysis with a multi-categorical profile antecedent. Differences among CLVS profiles for relative direct and indirect associations through injury, depression, and posttraumatic stress disorder (PTSD) to chronic pain disability were examined. Differences among profiles by SDOH were also explored. Compared to Profile 1 (Lowest CLVS), Profile 4 (Highest Target and Perpetrator) had significant relative direct and indirect effects through lifetime injuries and PTSD, with the highest mean scores for chronic pain disability and all mediators. Indirect effects through PTSD for Profiles 2 (second Lowest Target, Moderate Physical Partner Perpetrator) and 3 (second Highest Target, Low Psychological Perpetrator) were also significant. Masculine discrepancy stress, adverse housing, economic challenges, and substance use were significantly higher for Profile 4. These results demonstrate that high perpetration differentiates men most likely to have the highest chronic pain disability as indicated by direct and indirect pathways. Findings also highlight the need for trauma- and violence-informed approaches to chronic pain assessment and management to avoid re-traumatization. SDOH inequities may identify starting points for strength-based interventions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".