Evidence for the effectiveness of police-based pre-booking diversion programs in decriminalizing mental illness: A systematic literature review
Bibliographic record
Abstract
PURPOSE: People with mental illnesses are at a significantly greater risk of police arrest than the general population. This pattern of arrests has been associated with a phenomenon referred to as the criminalization of mental illness such that people with mental illnesses are inappropriately diverted to the criminal justice system rather than to treatment. To decrease arrests of people with mental illnesses experiencing a crisis, pre-booking diversion programs have been developed to intervene at the point of police contact. This systematic literature review examines the state of knowledge regarding the effectiveness of police-based pre-booking diversion programs by addressing the question, "What is the evidence for the effectiveness of police-based pre-booking diversion programs in reducing arrests (i.e., reducing criminalization) of people with mental illnesses?" METHODS: Systematic literature searches of seven databases were performed during May 2017. The searches sought to identify studies that examined the effectiveness of pre-booking diversion programs in decreasing arrests. A multi-phase screening process was completed independently by two pairs of reviewers as well as a risk of bias review. RESULTS: A total of 2,750 unique citations were identified. Of these, 4 met the inclusion/exclusion criteria; all were from the US. Three of the studies examined the effectiveness of Crisis Intervention Teams and one study looked at a mobile crisis program. Two of the studies were at moderate risk of bias and two at high risk. CONCLUSIONS: This review indicates that this line of inquiry is still developing. There are a number of gaps yet to be filled. The current evidence for the effectiveness of police-based pre-booking diversion programs in reducing arrests (i.e., reducing criminalization) of people with mental illnesses is limited. However, these studies indicate there is moderate evidence that these programs increase linkages to mental health services.
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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 | 0.025 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".