A trans-disciplinary approach to assessing police responses to mental heath crisis: development of the de-escalating persons in crisis competencies tool (DePICT)
Bibliographic record
Abstract
Introduction: Police interactions with individuals experiencing mental health crises are complex and can potentially involve safety risks. There are longstanding calls for enhanced training in deploying de-escalation strategies to reduce police use of force specifically in situations involving people in mental health crises. Given the absence of an existing protocol to evaluate de-escalation competencies specific to the realm of police mental health crisis intervention, the objective of this research was to develop and validate such a tool. Methods: Development of the framework emphasized a police and community co-design approach in which the identification of core competencies was driven by stakeholder focus groups, literature reviews, and best practices in adult-based education and experiential learning models. The tool was tested and modified using live-action simulations in which police officers were instructed to respond to five unique high-intensity mental health crisis scenarios. Internal consistency, interrater reliability, and concurrent validity were examined in a series of validation studies. Results: The resultant framework, the De-escalating Persons in Crisis Competencies Tool (DePICT™), is a 14-item rater-observer competency-based assessment designed to systematically measure a trainee's demonstrated ability to safely de-escalate and respond to a person in mental health crisis using relational policing approaches. Discussion: To better meet rising mental health crisis calls, police organizations have begun to deliver specialized training to enhance the abilities of frontline officers to recognize mental health problems and safely de-escalate crises. The DePICT™ is a reliable and valid competency-based assessment tool that, when used together in scenario training applications, contributes to the measurement, training, and acquisition of essential knowledge, skills and abilities expected of modern police officers and prepares them for real-world job demands.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".