Assessing protective factors in treated violent offenders: Associations with recidivism reduction and positive community outcomes.
Bibliographic record
Abstract
The present study examined the assessment of protective factors and their linkages to treatment change, institutional and community recidivism, and positive community outcomes in a high-risk treated sample of violent male offenders. Participants included 178 federally incarcerated adult male violent offenders who participated in a high-intensity violence reduction program and were followed up 10 years postrelease in the community. A collection of risk- and protective-factor measures were rated archivally at multiple time points-the Violence Risk Scale (Wong & Gordon, 1999-2003), Historical Clinical Risk Management-20 (Version 2; Webster, Douglas, Eaves, & Hart, 1997), Structured Assessment of Protective Factors (SAPROF; De Vogel, De Ruiter, Bouman, & De Vries Robbé, 2009), and Protective Factors (PF) List. Measures of community and institutional recidivism and positive community outcomes were coded. Large correlations were observed between risk and protection scores, suggesting shared risk variance. The SAPROF and PF List each predicted decreased community recidivism and, to a lesser degree, decreased institutional recidivism. Positive changes in protective factors were significantly associated with reductions in violent and general community recidivism and serious institutional misconducts after controlling for baseline scores. In addition, risk and protection scores significantly predicted most positive community outcomes; improvements in protective factors were linked to an increase in positive outcomes. Protective factors are more than the inverse of risk factors and might have important benefits in violence risk assessment and treatment planning when other positive community outcomes are considered. (PsycInfo Database Record (c) 2020 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".