Risks, strengths, gender, and recidivism among justice-involved youth: A meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: Debate ensues regarding female-specific risk and strength factors among adolescent offenders. Using meta-analysis, we examined whether risk and strength factors predicted recidivism differentially between male and female youth. METHOD: Database searches identified 22 studies, representing 50,601 justice-involved youth (11,952 females and 38,649 males) and a total of 584 effect sizes. RESULTS: For the global risk domains, there is some evidence for gender neutrality (i.e., risk factors predict to the same degree for both males and females) among most domains (e.g., antisocial peer relations, problematic family circumstances and parenting, substance abuse, antisocial personality/behavior, and antisocial attitudes/orientation). Although the global domains of mental health and child abuse were not significantly predictive for either gender, the global child abuse results trended in favor of predicting recidivism for females. When global risk domains were broken into indicators, some evidence for gender differences emerged (e.g., chronic alcohol use and family substance abuse predicted more strongly for females than for males). Last, gender comparisons among the global strength domains revealed that prosocial peers and the absence of substance abuse predicted success (i.e., no recidivism) for both genders, though a stronger effect emerged for males. In addition, education/employment strengths predicted success for males, whereas prosocial values predicted success for females. Limitations such as the lack of studies that defined constructs from the female experience, and the small number of primary studies are discussed. CONCLUSIONS: Advancing the future of gender informed practice with justice-involved youth will require careful consideration of both gender similarities and differences. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".