Mental Health Diagnoses and Recidivism in Paroled Offenders
Bibliographic record
Abstract
Although the issue of mental illness among offender populations has received attention in the last number of years, there are a number of issues related to mental illness among such groups that require more study. One such topic relates to the association between mental illness, actuarially assessed risk of recidivism, and observed rates of reoffending. In the present investigation, file information was reviewed to determine the presence of a variety of mental health conditions. Actuarially based risk assessment data were also collected for participants as well as information regarding suspension, new charges, and convictions. A sample of 136 offenders housed in a halfway house operated by Correctional Service of Canada was included in the present investigation. Results indicated very high rates of serious mental illness in this high-risk population. Offenders with borderline personality disorder and attention deficit hyperactivity disorder were significantly more likely to recidivate or be suspended. Suspensions refer to administrative decisions to place an offender in jail due to problematic behaviour (typically involving a breach of his release conditions or new charges/convictions). Offenders with a diagnosis of paraphilic disorder were significantly less likely to recidivate or be suspended. Results are discussed in light of the available literature.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".