A qualitative evaluation of a co-responding mobile crisis intervention team in a small Canadian police service
Bibliographic record
Abstract
Mobile Crisis Intervention Teams (MCITs) are meant to assist the police in responding appropriately to calls involving persons in crisis with the goal of providing short-term crisis management, as well as open doors to available resources for long-term support.Many police agencies across North America have implemented a MCIT, however, evaluations of these programs are limited.This thesis presents a qualitative evaluation of the South Simcoe Police Services' (SSPS) Crisis Outreach and Support Team (COAST) located in Bradford, Ontario.The COAST is based on the MCIT model and builds on a partnership between SSPS and two mental health organizations within the community (Canadian Mental Health Association [CMHA] and York Support Service Network [YSSN]).The evaluation consisted of interviews with various stakeholder groups including COAST members, SSPS senior leaders and frontline officers, and senior leaders from YSSN and CMHA, Barrie.Throughout the interviews, it became clear that there was a great need for the COAST in South Simcoe due to the increasing number of crisis calls in which the police were required to respond.There was strong consensus among stakeholders that the COAST provides a better service than a conventional police response to those experiencing crises while also helping to bridge the gap between the police and mental health care providers who traditionally work in silos.Despite the perceived benefits of the COAST, the lack of resources was seen as a major challenge.That being said, the current findings still suggest that co-responding teams may improve the way the police respond to individuals in crisis.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".