Posttreatment Victimization and Violence Among Adolescents Following Residential Drug Treatment
Bibliographic record
Abstract
This article examines the relationships among experiences of childhood abuse, psychiatric disorders, self-reported victimization, and violent behavior, with a focus on gender differences. Data were obtained from treatment entry and 5-year post-treatment interviews of 446 adolescent clients in therapeutic community (TC) drug treatment programs throughout the United States and Canada. Fifty-eight percent of the sample indicated that they engaged in serious violent behaviors (e.g., beatings, threatening or using weapons against other people, or violent crimes such as assaults, rapes, murders) in the 5 years following their separation from TC treatment. Multivariate logistic regression analyses revealed that victimization in the posttreatment period was the most significant factor associated with violent behavior, and pretreatment childhood abuse experiences and psychiatric disorders were not significantly related to the odds of violent behavior. There were significant gender differences in self-reported victimization and violent behavior. The findings suggest that violence in young adulthood for males is related to increasing involvement in violent lifestyles that include drug trafficking, while violence among females is associated with the social and psychological consequences of drug involvement and victimization. High rates of violent involvement and victimization among former adolescent clients suggests the utility of incorporating interventions such as safety-oriented strategies for females or interventions that address involvement in the drug use lifestyles (i.e., use and dealing) for both males and females into residential treatment to reduce the likelihood of future 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".