Changes in Police Contact After Admission to the Assertive Community Treatment With Police Integration Program
Bibliographic record
Abstract
OBJECTIVE: The assertive community treatment (ACT) model has been shown to successfully reduce hospitalizations and increase quality of life for individuals living with severe mental illness (e.g., psychotic disorders or schizophrenia). Participation in ACT alone, however, does not decrease clients' contact with the criminal justice system. ACT with police integration (ACT-PI), a novel model that entails integrating police officers into existing ACT teams, is viewed by clients and staff as an acceptable method to reduce criminal encounters with unknown police officers. The aim of the study was to examine changes in multiple types of police encounters before and after admission to the program. METHODS: The authors collected data from 448 unique ACT-PI clients who had at least one police contact between 2008 and 2019. RESULTS: The number of overall police encounters significantly decreased, in particular for non-mental health-related occurrences. The number of mental health-related police encounters increased. These changes were especially evident among clients whom police identified by their race, suggesting that the ACT-PI program may have nuanced effects on outcomes for this group. CONCLUSIONS: These findings provide further support for the ACT-PI program as an effective method to reduce the criminalization of persons with severe mental illness and to shift remaining police responses toward mental health-related interventions.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".