Identifying gender specific risk/need areas for male and female juvenile offenders: Factor analyses with the Structured Assessment of Violence Risk in Youth (SAVRY).
Bibliographic record
Abstract
By constructing risk assessment tools in which the individual items are organized in the same way for male and female juvenile offenders it is assumed that these items and subscales have similar relevance across males and females. The identification of criminogenic needs that vary in relevance for 1 of the genders, could contribute to more meaningful risk assessments, especially for female juvenile offenders. In this study, exploratory factor analyses (EFA) on a construction sample of male (n = 3,130) and female (n = 466) juvenile offenders were used to aggregate the 30 items of the Structured Assessment of Violence Risk in Youth (SAVRY) into empirically based risk/need factors and explore differences between genders. The factor models were cross-validated through confirmatory factor analyses (CFA) on a validation sample of male (n = 2,076) and female (n = 357) juvenile offenders. In both the construction sample and the validation sample, 5 factors were identified: (a) Antisocial behavior; (b) Family functioning; (c) Personality traits; (d) Social support; and (e) Treatability. The male and female models were significantly different and the internal consistency of the factors was good, both in the construction sample and the validation sample. Clustering risk/need items for male and female juvenile offenders into meaningful factors may guide clinicians in the identification of gender-specific treatment interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".