Examining police, health, and mental health crisis response teams
Bibliographic record
Abstract
Scarce community mental health resources have led to people in crisis (PIC) overusing the emergency department (ED) and encountering police more frequently. To divert PIC from the ED and criminal justice system, and support them in their community, police services have implemented crisis response teams (CRTs). CRTs refer to police, health and mental health crisis response. Evidence of CRTs’ effectiveness in achieving their desired outcomes is limited, mixed, and/or anecdotal. I completed three studies using various theoretical and methodological approaches, which included: (a) a critical interpretative synthesis (CIS) of the conditions under which CRTs are formed, their features, and their outcomes; (b) a policy analysis using a case study design to examine how and why a CRT model was adopted in Hamilton, Canada; and (c) a what’s the problem represented to be (WPR) critical policy analysis of why police are implicated in crisis response. The CIS presents a conceptual framework depicting how unresolved structural conditions produce system- and individual-level challenges. Second, the case study examines the mobile crisis rapid response team (MCRRT) development in Hamilton. The analysis shows that initiatives that incrementally expand on the boundaries of existing programs are likely to be adopted. Third, drawing on WPR, we excavate problem representations within policy and policy-related texts to understand why police-based CRTs are expanded in Ontario. When mental health is framed in terms of safety and implicated within discourses about risk and danger police intervention is legitimized. Collectively, these studies provide a theoretical framework connecting structural, system, and individual factors most relevant to CRTs; demonstrate that an incremental approach to CRT adoption did not disrupt existing system arrangements; and problematizations within government policies that legitimize police in mental health crisis response.
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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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.023 | 0.025 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".