Risky business: a regional comparison of the levels of risk and service needs of sexually offending youth
Bibliographic record
Abstract
Considerable attention has focussed on identifying individual factors associated with, or predictive of, sexual offending (e.g., Efta-Breitbach & Freeman, 2004). In light of these individual factors, clinicians and researchers have developed standardized instruments for assessing the risk posed by sexually offending youth. Two such instruments are the Juvenile Sex Offender Assessment Protocol-II (J-SOAP-II; Prentky & Righthand, 2003) and the Estimate of Risk of Adolescent Sexual Offence Recidivism Version 2.0 (ERASOR-II; Worling & Curwen, 2001). In addition to individual factors, research on crime has demonstrated that structural factors within the community may be important determinants of sexual and non-sexual offending (e.g., McCarthy, 1991; Ouimet, 1999; Shaw & McKay, 1942; Wirth, 1938). Therefore, the purpose of this study was twofold: (a) to compare the psychometric properties of two newly developed risk assessment instruments (i.e., J-SOAP-II and ERASOR-II) and (b) to use the better instrument to compare the levels of risk posed by sexually offending youth in 3 neighbouring, but diverse communities. Using file information, the J-SOAP-II and ERASOR-II were scored on 84 adolescent males between the ages of 11 and 20 years who had committed a sexual offence and received treatment at Youth Forensic Psychiatric Services (YFPS) in the Greater Vancouver Area (GVA; n = 30), Central Okanagan (CO; n = 26), and Thompson Nicola region (TN; n = 28). Calculations of interrater reliability and item-total correlations indicated that the J-SOAP-II was a better assessment instrument for this sample of offenders. Consequently, further regional analysis of risk was conducted using the J-SOAP-II data. Results indicated that although there were no regional differences among the severity and history of sexual offending, TN youth generally had a greater number of risk factors than did youth in CO and GVA. Specifically, youth in TN were found to be higher risk in the areas of intervention, general problem behaviour, iii and family/environment dynamics. These results suggest that to better understand youth who commit sexual offences and to provide appropriate prevention and intervention strategies for individual offenders and their communities, youth should not be evaluated in isolation from their social and community context. Recommendations for practice are discussed.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".