Physical and Sexual Abuse Issues among Youths with Substance Use Problems
Bibliographic record
Abstract
OBJECTIVES: To evaluate the prevalence of reported physical and sexual abuse among youths with substance use problems, to explore whether youths report relying on substances to cope with the abuse, and to examine whether individual factors related to substance use were associated with the outcome measures of reported physical abuse, sexual abuse, and using substances to cope. METHOD: We assessed 287 male and female youths (age 14 to 24 years) who presented for help for substance use problems, using a semistructured interview that focused on substance use, history of previous sexual and physical abuse, and coping strategies. RESULTS: One-half of the female youth substance abusers reported having been sexually abused (50.0%), while male youth substance users reported a significantly lower rate (10.4%). Similarly, one-half of the female youths had a history of physical abuse (50.5%), and males again had a lower rate (26.0%). Of those who endorsed a history of abuse, more females (64.7%) than males (37.9%) reported using substances to cope with the trauma. Specific associations between the outcome measures and substance use variables were found for youths in both sexes. CONCLUSION: These findings underscore the importance of why clinicians should explore abuse issues with substance-using youth of both sexes. Identifying concurrent factors will help provide better intervention strategies. Suggestions for assessing sexual and physical abuse in youths with substance use disorders are provided.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".