Developing Community Co-designed Scenario-Based Training for Police Mental Health Crisis Response: a Relational Policing Approach to De-escalation
Bibliographic record
Abstract
Abstract Using the current empirical landscape of police responses to people in mental health crisis as a backdrop, this methods paper makes an argument for the central role of collaborative co-design and production by diverse community experts and stakeholders to build transformative specialized training for frontline officers. Subject matter experts (SMEs) from across key domains participated in focus groups and curriculum creation, with outputs being the co-development of a conceptual approach and an innovative experiential learning training program. Part 1 unpacks the team’s conceptual development of a relational policing approach . This humanized method is shaped by procedural justice, trauma-informed, person-centred, and cultural safety frameworks. Part 2 details the co-production of a novel problem-based training method for a police service in Southern Ontario, Canada. The program centres on the acquisition of core competencies related to relational policing, de-escalation, and mental health crisis response. The training was designed to bring learners through a spectrum of authentic crisis scenarios: from observer-participant scenarios informed by Forum Theatre methods and targeted SME feedback to a range of high-fidelity assessment simulations that test officers’ abilities to effectively communicate, de-escalate, and make decisions under stress. This program offers repeated opportunities for officers to practice alternative crisis management strategies in scenarios that might otherwise result in the use of force.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".