Assessment and Treatment of Adolescents Who Sexually Offend
Bibliographic record
Abstract
Some adolescents who have committed a sexual crime are placed by the courts in secure residential settings. Given the heterogeneity of this client group, it is important for clinicians in these settings to complete comprehensive assessments to determine the course and content of specialized treatment, if necessary. With a focus on residential care, suggestions are provided for the assessment of strengths, risks, and needs. Particular attention is paid to issues related to informed consent, interviewing, and risk assessment. Also reviewed are various treatment issues with implications in secure settings, including the delivery of therapeutic services, use of manuals, therapeutic relationships and context, and self-care for providers. The growing evidence base for cognitive-behavioral treatment for adolescents who have sexually offended is outlined, and common treatment goals for youth who have offended sexually are critically examined. With an emphasis on treatment tailored to the unique needs of each adolescent, suggestions are offered regarding goals such as increasing accountability, recovery from posttraumatic distress, developing offense-prevention strategies, and enhancing awareness of victim impact, prosocial sexual attitudes, and healthy sexual interests. Additional issues that are considered with implications for clinicians working in secure settings include sibling sexual abuse and offenses involving child abuse imagery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".