Risk and Protective Factors for Recidivism Among Juveniles Who Have Offended Sexually
Bibliographic record
Abstract
Literature on risk factors for recidivism among juveniles who have sexually offended (JSOs) is limited. In addition, there have been no studies published concerning protective factors among this population. The purpose of this study was to examine the relationship of risk and protective factors to sexual and nonsexual recidivism among a sample of 193 male JSOs (mean age = 15.26). Youths were followed for an average of 7.24 years following discharge from a residential sex offender treatment program. The risk factor opportunities to reoffend, as coded based on the Estimate of Risk of Adolescent Sexual Offense Recidivism, was associated with sexual recidivism. Several risk factors (e.g., prior offending; peer delinquency) were associated with nonsexual recidivism. No protective factors examined were associated with sexual recidivism, although strong attachments and bonds as measured by the Structured Assessment of Violence Risk in Youth was negatively related to nonsexual recidivism. These findings indicate that risk factors for nonsexual recidivism may be consistent across both general adolescent offender populations and JSOs, but that there may be distinct protective factors that apply to sexual recidivism among JSOs. Results also indicate important needs for further research on risk factors, protective factors, and risk management strategies for JSOs.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".