Pre‐arrest diversion of people with mental illness: literature review and international survey
Bibliographic record
Abstract
Mental health diversion is a process where alternatives to criminal sanctions are made available to persons with mental illness (PMI) who have come into contact with the law. One form of mental health diversion is pre-arrest, in which the police use their discretion in laying charges. Concomitant with the growth of pre-arrest diversion programs is a growing body of research devoted to the phenomenon. The purpose of this paper is to review the existing literature of pre-arrest diversion, and to report the results of an international survey of pre-arrest diversion programs we conducted to identify evidence-based practices. On the basis of our review and survey, we note that successful pre-trial programs appear to integrate relevant mental health, substance abuse and criminal justice agencies by having regular meetings between key personnel from the various agencies. Often, a liaison person with a mandate to effect strong leadership plays a key role in the coordination of various agencies. Streamlining services through the creation of an emergency drop-off center with a no-refusal policy for police cases is seen as crucial. While there is some indication that mentally ill offenders benefit from their participation in this form of diversion, the evaluative literature has not yet achieved the "critical mass" necessary to create generalizable, evidence-based knowledge. The absence of generally agreed-upon outcomes could lead to the inequitable application of basic principles of diversion. We suggest that indicators, benchmarks, and outcomes must be agreed upon if a comprehensive understanding of pre-arrest programs is to emerge.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".