Exposure to Gun Violence: Associations with Anxiety, Depressive Symptoms, and Aggression among Male Juvenile Offenders
Bibliographic record
Abstract
Objective: To examine whether at-risk male youth experience increases in anxiety, depressive symptoms, and aggression during years when they are exposed to gun violence, adjusting for relevant covariates.Method: Participants were 1,216 male, justice-involved adolescents who were recently arrested for the first time for a moderate offense. They were interviewed 9 times over 5 years. Fixed effects (within-individual) regression models were used to estimate concurrent associations between exposure to gun violence and three outcomes: depressive symptoms, anxiety symptoms, and aggression (both overall and separately for proactive and reactive aggression). The reverse direction (anxiety, depressive symptoms, and aggression predicting gun violence exposure) was also modeled.Results: After controlling for covariates, exposure to gun violence was significantly associated with increases in reactive aggression and, to a lesser extent, increases in proactive aggression. In addition, gun violence exposure was associated with increased anxiety but not depressive symptoms. We found no support for the reverse direction.Conclusions: At-risk males experienced significant increases in anxiety and aggression (particularly reactive aggression) during years when they are exposed to gun violence, even after accounting for several potential confounding factors. The greater impact on reactive aggression suggests that exposure to gun violence may affect self-regulation and/or social information processing. The analyses shed light on the less-visible damage wrought by gun violence and underscore the importance of mental health screening and treatment for youth who have been exposed to violence – especially gun violence – both to assist individual youths and to disrupt cycles of violence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".