Mental Disorder and Threats Made by Noninstitutionalized People With Weapons in the National Comorbidity Survey Replication
Bibliographic record
Abstract
Controversy exists as to whether mental disorders are associated with a higher risk of violent behavior. Data from the nationally-representative National Comorbidity Survey Replication was examined. Multiple logistic regression was used to determine whether mood, anxiety, impulse control, and substance use disorders were associated with a higher rate of potentially violent behavior as assessed by threatening others with a gun or other weapon. After adjusting for sociodemographic factors, an association was found between mood, anxiety, impulse control, and substance use disorders and the rate of threatening others. A significant association was found between threats made against others with a gun and both substance use disorders (adjusted odds ratio [AOR] 2.27; 95% confidence interval [CI] 1.62-3.20) and impulse control disorders (AOR 2.67; 95% CI 1.95-3.66). Threats made against others with any other type of weapon were significantly associated with any anxiety (AOR 1.76; 95% CI 1.34-2.31), substance (AOR 2.63; 95% CI 1.87-3.71), or impulse control disorder (AOR 2.49; 95% CI 1.96-3.18). Of the disorders studied, social phobia, specific phobia, and impulse control disorders seemed to have their onset before the act of threatening others with weapons. This finding was also true for those who had attempted suicide. Further research is needed to determine whether treatment of mental disorders decreases the risk of violence in this population.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".