Influences of victimization and comorbid conditions on substance use disorder outcomes in justice-involved youth: A discrete time survival mixture analysis
Bibliographic record
Abstract
To understand how exposure to victimization during adolescence and the presence of comorbid psychological conditions influence substance use treatment entry and substance use disorder diagnosis from 14 to 25 years old among serious juvenile offenders, this study included 1,354 serious juvenile offenders who were prospectively followed over 7 years. Growth mixture modeling was used to assess profiles of early victimization during adolescence (14-17 years). Discrete time survival mixture analysis was used to assess time to treatment entry and substance use disorder diagnosis. Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) were used as predictors of survival time. Mixture models revealed three profiles of victimization: sustained poly-victimization, moderate/decreasing victimization, and low victimization. Youth in the sustained poly-victimization class were more likely to enter treatment earlier and have a substance use diagnosis earlier than other classes. PTSD was a significant predictor of treatment entry for youth in the sustained poly-victimization class, and MDD was a significant predictor of substance use disorder diagnosis for youth in the moderate/decreasing victimization class. Therefore, substance use prevention programming targeted at youth experiencing poly-victimization in early adolescence-especially those who have PTSD or MDD-is needed.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".