Do Community Structural Characteristics Moderate the Association Between Mental Health and the Frequency and Severity of Violent-Behavioral Outcomes in Community Respondents?
Bibliographic record
Abstract
In this prospective study, we examined the association between three types of mental health symptom clusters (i.e., psychotic, internalizing, and externalizing) and the frequency and severity of violent-behavioral outcomes, and whether community disadvantage, residential instability, and criminogenic facility density moderated these associations. Study data were derived from 258 community-dwelling adults nested in 60 postal forward sortation areas (FSAs) in a large metropolitan area in Western Canada who were assessed twice over a 6-month period. In addition, census and administrative data were obtained on the same areas. Controlling for sociodemographic characteristics (i.e., age, gender, ethnicity, relationship status, and employment status), lifetime history of violent-behavioral outcomes, and community structural characteristics, internalizing and externalizing mental health symptoms were significantly positively associated with the frequency and severity of subsequent violence perpetration and with the severity of subsequent violent victimization. Several significant interactions were observed: internalizing symptoms increased the risk of frequent and severe violence perpetration in FSAs with high but not low disadvantage, and externalizing symptoms increased the risk of frequent violent victimization in FSAs with a high but not low criminogenic facility density. Only the interactive association of internalizing symptoms and community disadvantage with the severity of violence perpetration, however, remained significant after Bonferroni correction was applied. These findings provide tentative support that associations between mental health and violent-behavioral outcomes can vary with community context. The implication of these findings for assessing and managing violent-behavioral outcomes in the community is discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".