Co‐responding police–mental health programmes: Service user experiences and outcomes in a large urban centre
Bibliographic record
Abstract
As police officers are often the first responders to mental health crises, a number of approaches have emerged to support skilled police crisis responses. One such approach is the police-mental health co-responding team model, whereby mental health nurses and police officers jointly respond to mental health crises in the community. In the present mixed-method study, we evaluated outcomes of co-responding team interactions at a large Canadian urban centre by analysing administrative data for 2743 such interactions, and where comparison data were available, compared them to 16 226 police-only team responses. To understand service user experiences, we recruited 15 service users for in-depth qualitative interviews, and completed inductive thematic analysis. Co-responding team interactions had low rates of injury and arrest, and compared to police-only teams, co-responding teams had higher overall rates of escorts to hospital, but lower rates of involuntary escorts. Co-responding teams also spent less time on hospital handovers than police-only teams. Service users valued responders with mental health knowledge and verbal de-escalation skills, as well as a compassionate, empowering, and non-criminalizing approach. Current findings suggest that co-responding teams could be a useful component of existing crisis-response systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".