Cumulative Lifetime Violence Severity and Chronic Pain in a Community Sample of Canadian Men
Bibliographic record
Abstract
OBJECTIVE: To create a descriptive profile of chronic pain severity in men with lifetime cumulative violence histories, as a target and/or a perpetrator, and investigate how chronic pain severity is associated with and predicted by lifetime cumulative violence severity and known determinants of chronic pain. METHODS: Analysis of variance and binary logistic regression were performed on data collected in an online survey with a community convenience sample of 653 men who reported experiences of lifetime violence. RESULTS: The prevalence of high-intensity / high-disability pain in men with lifetime violence was 35.8%. Total Cumulative Lifetime Violence Severity-44 (CLVS-44) scores were significantly associated with high-intensity / high-disability chronic pain measured by the Chronic Pain Grade Scale (odds ratio= 8.40). In a model with 10 CLVS-44 subscale scores, only psychological workplace violence as a target (adjusted odds ratio [aOR]= 1.44) and lifetime family physical violence as a target (aOR= 1.42) significantly predicted chronic pain severity. In a multivariate model, chronic pain severity was predicted by CLVS-44 total score (aOR= 2.69), age (aOR= 1.02), injury with temporary impairment (aOR= 1.99), number of chronic conditions (aOR= 1.37), and depressive symptoms (aOR= 1.03). CONCLUSION: The association between lifetime cumulative violence severity and chronic pain severity in men is important new information suggesting the need for trauma- and violence-informed approaches to assessment and intervention with men. This is the first analysis using CLVS-44 subscales to understand which configurations of lifetime cumulative violence may be most predictive of chronic pain severity; further investigation is needed to confirm these findings.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".