Recidivism Rates of Treated, Non-Treated and Dropout Adolescent Who Have Sexually Offended: a Non-Randomized Study
Bibliographic record
Abstract
The primary objective of this study was to evaluate the effectiveness of a cognitive-behavioral treatment in reducing recidivism by adolescents who have sexually offended (ASO). A secondary objective was to determine whether typologies based on victim age (child, adult/peer, mixed) and relationship (intrafamilial, extra familial, intra/extra familial) discriminate ASO in terms of response to treatment and recidivism. The sample comprised 327 adolescents 12–18 years old (M = 15.8 years, SD = 1.9) who were evaluated in an outpatient clinic after committing a contact sexual assault. Official data on recidivism (criminal charges) was collected after a follow-up period of 21–162 months (M = 7.8 years, SD = 32.2). Survival analysis indicated that adolescents who completed treatment (n = 62) had a recidivism rate for violence (including sexual violence) almost half that of adolescents who had either not completed the treatment or not received treatment (n = 261), (16.1 vs. 30.7%). Neither of the two typologies studied had any effect on the completion of treatment. However, sexual aggression against adults/peers was associated with an increased probability of violent re-offending. These results confirm the effectiveness of this cognitive-behavioral treatment —which targets risk factors associated with sexual aggression as well as those associated with violence in general—in ASO.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".