Matching Mobile Crisis Models to Communities: An Example from Northwestern Ontario
Bibliographic record
Abstract
Police are often the first to encounter individuals when they are experiencing a mental health crisis. Other professionals with different skill sets, however, may be needed to optimize crisis response. Increasingly, police and mental health agencies are creating co-responder teams (CRTs) in which police and mental health professionals co-respond to crisis calls. While past evaluations of CRTs have shown promising results (e.g. hospital diversions; cost-effectiveness), most studies occurred in larger urban contexts. How CRTs function in smaller jurisdictions, with fewer complementary resources and other unique contextual features, is unknown. This paper describes the evaluation of a CRT operating in a geographically isolated and northern mid-sized city in Ontario, Canada. Data from program documents, interviews with frontline and leadership staff, and ride-along site visits were analyzed according to an extended Donabedian framework. Through thematic analysis, 12 themes and 11 subthemes emerged. Overall, data showed that the program was generally operating and supporting the community as intended through crisis de-escalation and improved quality of care, but it illuminated potential areas for improvement, including complementary community-based services. Data suggested specific structures and processes of the embedded CRT model for optimal function in a northern context, and it demonstrated the transferability of the CRT model beyond large urban centres. This research has implications for how communities can make informed choices about what crisis models are best for them based on their resources and context, thus potentially improving crisis response and alleviating strain on emergency departments and systems.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".