From Blue to Green : A Case Study of a Non-Police Crisis Intervention Program in Toronto
Bibliographic record
Abstract
For many years, the police have become the default first responder to mental health crisis calls in Canada and many other jurisdictions around the world. Phrases such as“street-corner psychiatrist” or “the gatekeeper of the mental health care system” are often used to describe the overreliance on police in responding to mental health crises. The increasing interactions between police and person with mental illnesses (“PMIs”) and the nature of these interactions in recent years have called into question the appropriateness of the role of police in responding to mental health-related service calls. In a recent effort to decrease police involvement in mental health crises, the City of Toronto piloted a non-police, community-led crisis intervention program called the Toronto Community Crisis Service (TCCS) in 2021. This exploratory qualitative study aims to capture the perspectives of policy and frontline staff from the TCCS program to answer the question: “What are the key characteristics of a crisis intervention program to ensure effective responses to people in mental health crises?” Findings from this study suggest that an effective crisis intervention service should have the following characteristics: timely and accessibility of service, responding without law enforcement accompaniment, connecting PMIs to facility-based care as needed through warm hand-offs, empowering PMIs to make their own choices by taking a client-centred approach, building community trust and rapport through engagement and psychoeducation, and providing flexible services to meet the unique needs of PMIs. Crisis intervention provides an opportunity to positively effect change at a turning point in an individual life to help decrease the likelihood of such behaviour in the future. The success of a crisis intervention program is only as great as the resources behind it. Inter-governmental collaboration and investment in community-based resources, including shelter beds, housing, stabilization centers, and support to front-line staff are crucial to ensure a consistent continuum of care for PMIs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.042 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".