Examining implementation of mobile, police-mental health crisis intervention teams in a large urban center
Bibliographic record
Abstract
BACKGROUND: Mobile Crisis Intervention Teams (MCITs) have emerged as a police and mental health system co-response to assist police in responding to individuals experiencing mental health crises. There is a gap in knowledge regarding the critical program components that contribute to successful MCIT implementation. AIMS: This evaluation study aimed to understand processes of implementation of a multi-site MCIT program in a large urban center and to identify program strengths and challenges, as well as levels of satisfaction in service delivery. METHODS: Fifty-seven stakeholders participated in qualitative interviews and focus groups, including: MCIT consumers and staff, individuals from the health system, police services, and community organizations. RESULTS: Overall, program stakeholders perceived the MCIT program positively and viewed it as meeting its key goals. The implementation evaluation has highlighted the importance of respectful interaction with consumers, cross-sector training and collaboration, and ensuring clarity in program mandate and staff roles. These program components can continue to be built upon to improve MCIT service delivery. CONCLUSIONS: Future studies should further evaluate the role of key strengths in MCIT program implementation as well as the impact of recommended improvements on program outcomes.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".