A Validation of the Dynamic Risk Assessment for Offender Re-entry (DRAOR) for use with Offenders with Mental Disorder
Bibliographic record
Abstract
Persons with mental disorders face widespread challenges in their lives, including disproportionate involvement in the criminal justice system.As there has been ongoing scholarly debate regarding relevant criminal risk factors for offenders with mental disorders, better understanding their experiences with the criminal justice system is essential to ensure they are being managed appropriately.The goal of the current doctoral research was to validate the Dynamic Risk Assessment for Offender Re-entry (DRAOR) for use with offenders with a mental disorder.A sample of 961 parolees in the state of Iowa (49.7% being diagnosed with a mental disorder) was used to achieve this goal.Findings showed that, while offenders with a mental disorder were assessed as having higher dynamic risk and lower protective factors, they were equally likely to recidivate compared to those with no mental disorder.While the DRAOR had utility with offenders who were not diagnosed with a mental disorder, results were less positive for those with a diagnosis.Discrimination analyses found that the DRAOR was only able to weakly discriminate between those who did or did not violate the conditions of their release, while calibration analyses found that the DRAOR may be under-classifying lowerscoring offenders with a mental disorder and over-classifying higher-scoring offenders with a mental disorder.The consideration of current mental health-related problems augmented the prediction of technical violations over DRAOR assessments for offenders with mental disorder, pointing to the possibility that there may be other factors relevant to risk prediction for this sub-population.Analyses focused on assessments over time found that, regardless of the presence of a mental disorder, offenders' levels of dynamic risk, but not protective factors, changed over multiple assessments.Subsequent analysis found iii DRAOR VALIDATION FOR OFFENDERS WITH MENTAL DISORDER that while DRAOR change scores significantly predicted future technical violations, they did not predict new charges.Overall, these findings point to the need for parole officers to exercise caution with using the DRAOR with clients who have a diagnosed mental disorder.Further research is needed to better understand the underlying reasons why the DRAOR does not work as well with offenders with mental disorders compared to those without mental disorders.iv DRAOR VALIDATION FOR OFFENDERS WITH MENTAL DISORDER Acknowledgements I would like to express my deepest thanks to my supervisor, Dr. Ralph Serin.Your enthusiasm and support over the past eight years of working together has been essential in developing my skills as a researcher.I am so grateful that you took me on.I would also like to
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".