Benefits and Drawbacks of Police Integration Into Assertive Community Treatment Teams
Bibliographic record
Abstract
OBJECTIVE: Assertive community treatment (ACT) teams provide outreach services to individuals coping with severe mental illness. Because such individuals are at increased risk for involvement with law enforcement, a model that integrates police officers into ACT teams (ACT-PI) was developed for ACT teams serving clients with or without forensic involvement. The goal of this study, conducted in British Columbia, was to evaluate the benefits and drawbacks of the ACT-PI model. METHODS: Qualitative semistructured interviews were conducted with 21 ACT-PI clients (in 2017) and 22 ACT-PI staff (in 2018). Thematic analyses identified key themes related to the benefits and drawbacks of officer integration into the ACT-PI model. RESULTS: Perceived benefits of police integration were opportunities for relationship building between officers and clients, improved safety, more holistic care due to embeddedness (i.e., effective interagency collaboration between police and health care providers), the prevention of future problems, and police officers' authority enhancing compliance. Perceived drawbacks included risk for legal consequences, stigma from police interaction, escalating distress of clients, low officer availability, and the risk for changing the nature of ACT teams. CONCLUSIONS: Participants reported that the model of officer integration into ACT-PI teams may improve both client and staff well-being. In some communities, and with certain precautions, ACT-PI may be a viable model for ACT teams serving clients with and clients without a history of forensic involvement.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".