Policing Mental Health: The Composition and Perceived Challenges of Co-Response Teams and Crisis Intervention Teams in the Canadian Context
Bibliographic record
Abstract
Due to an increase in interactions between the police and persons with perceived mental illness (PwPMI), police services have begun deploying specialized crisis responses to more adequately address these calls.One such response is a Crisis Intervention Team (CIT) that is comprised of frontline officers who are specially trained on mental health; another is a Co-Response Team (CRT) where an officer is paired with a mental health practitioner.With police services presumably shifting scarce resources to deploy these responses, it is paramount to understand the challenges they may endure.With little Canadian research on these responses to-date, the purpose of this paper is to document which Canadian police services deploy these responses and how their composition varies by jurisdiction, as well as their perceived challenges.Through a mixed methodological approach, the results indicate that most of the participating services deploy varying compositions of a CIT and/or CRT, but are perceived to endure a variety of challenges which may impede the overall success of these responses.A call for future research is made which may assist Canadian police services in addressing some of the identified challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.043 | 0.013 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".