The use of a brief mental health screener to enhance the ability of police officers to identify persons with serious mental disorders
Bibliographic record
Abstract
Police agencies in Canada and elsewhere have received much criticism over how they respond to persons with serious mental disorders. The adequacy of training provided to police officers on mental health issues and in particular on recognizing indicators of serious mental disorders has been a major concern. This paper describes the process that led to the development of a new brief mental health screener (interRAI Brief Mental Health Screener, BMHS) designed to assist police officers to better identify persons with serious mental disorders. The interRAI BMHS was developed in collaboration with interRAI, an international, not-for-profit consortium of researchers. The government of Ontario had previously partnered with interRAI to develop and implement the Resident Assessment Instrument for Mental Health (RAI-MH), the assessment system mandated for use on all persons admitted into inpatient psychiatric care in the province. Core items on the interRAI BMHS were obtained through analysis (N=41,019) of RAI-MH data together with input from representatives from health care, police services, and patient groups. Two police services in southwestern Ontario completed forms (N=235) on persons thought to have a mental disorder. Patient records were later accessed to determine patient disposition. The use of summary and inferential statistics revealed that the variables significantly associated with being taken to hospital by police included performing a self-injurious act in the past 30days, and others being concerned over the person's risk for self-injury. Variables significantly associated with being admitted included abnormal thought process, delusions, and hallucinations. The results of the study indicate that the 14-variable algorithm used to construct the interRAI BMHS is a good predictor of who was most likely to be taken to hospital by police officers and who was most likely to be admitted. The instrument is an effective means of capturing and standardizing police officer observations enabling them to provide more and better quality information to emergency department (ED) staff. Teaching police officers to use the form constitutes enhanced training on major indicators of serious mental disorders. Further, given that items on the interRAI BMHS are written in the language of the health system, language acts as common currency between police officers and ED staff laying the foundation for a more collaborative approach between the systems.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".