A Controlled Before-and-after Evaluation of a Mobile Crisis Partnership between Mental Health and Police Services in Nova Scotia
Bibliographic record
Abstract
OBJECTIVES: Police are often the front-line response to people experiencing mental health crises. This study examined the impact of an integrated mobile crisis team formed in partnership between mental health services, municipal police, and emergency health services. The service offered short-term crisis management, with mobile interventions being attended by a plainclothes police officer and a mental health professional. METHODS: We used a mixed-methods design encompassing: a controlled before-and-after quantitative comparison of the intervention area with a control area without access to such a service, for 1 year before and 2 years after program implementation; and qualitative assessments of the views of service recipients, families, police officers, and health staff at baseline and 2 years afterward. RESULTS: The integrated service resulted in increased use by people in crisis, families, and service partners (for example, from 464 to 1666 service recipients per year). Despite increased service use, time spent on-scene and call-to-door time were reduced. At year 2, the time spent on-scene by police (136 minutes) was significantly lower than in the control area (165 minutes) (Student t test = 3.4, df = 1649, P < 0.001). After adjusting for confounders, people seen by the integrated team (n = 295) showed greater engagement than control subjects as measured by outpatient contacts (b = 1.3, chi square = 92.7, df = 1, P < 0.001). The service data findings were supported by the qualitative results of focus groups and interviews. CONCLUSIONS: Partnerships between the police department and mental health system can improve collaboration, efficiency, and the treatment of people with mental illness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".