Experiments in Change: Pretrial Diversion of Offenders with Mental Illness
Bibliographic record
Abstract
OBJECTIVE: Our objective was to study the outcomes experienced by 2 communities after implementing pretrial diversion of offenders with mental illness. METHOD: The same method of diversion was implemented in a predominately urban and a predominantly rural county. We collected retrospective clinical and offence data from pretrial diversion assessments conducted in court. As well, we measured outcome for the diversion procedure in terms of actual vs expected rates of recidivism. RESULTS: Prior psychiatric treatment was associated with the diverted group, and a criminal history was associated with the nondiverted group. In the larger, urban county the diversion option was offered more often to persons with psychoses, mood disorders, and minor offenses. Conversely, in the smaller rural county diversion was offered most often to persons accused of serious offenses. The recidivism found in urban and rural diverted groups after a year of supervised care was only 2% to 3%, but the rate of use of diversion in both counties was low, owing to selection biases. CONCLUSION: Pretrial diversion of offenders with mental illness accused of minor crimes is eminently feasible for both urban and rural settings, provided that police, crown, and treatment policies are coordinated to favour the treatment option rather than prosecution.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".