Examining the Contribution of Responsivity Factors to the Dynamic Risk Assessment for Offender Re-entry
Bibliographic record
Abstract
The risk-need-responsivity model has been shown to be an effective method for reducing recidivism rates (Andrews & Bonta, 2010).While the risk and need principles have garnered much attention in previous research, responsivity factors are not as well understood, especially when considering specific responsivity factors.The current study examined the use of three specific responsivity factors -trauma, mental health, and selfefficacy in conjunction with the Dynamic Risk Assessment for Offender Re-entry (DRAOR) in predicting recidivism and informing case planning and management.Archival data were used from the Iowa Department of Corrections database to create a sample of male justice-involved persons (N = 3,703) who had been assessed within the first year of their supervision start date.Linear regressions revealed relationships between all three responsivity factors and both total DRAOR scores and items of interest.Logistic regressions revealed relationships between all three responsivity factors and recidivism.However, only trauma and self-efficacy incrementally predicted recidivism when added to the DRAOR.The overall best model for predicting recidivism included total DRAOR scores, trauma, and self-efficacy.When considering all relationships examined, the current study demonstrates the usefulness of these three items in case management and planning as well as the importance of considering specific responsivity factors and attending to needs in order to assist justice-involved persons in achieving better outcomes.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".