Costs and Savings Associated With the Police Use of the interRAI Brief Mental Health Screener
Bibliographic record
Abstract
Background:The police response to calls for service identified as being related to mental health continues to be highly controversial. Strategies to improve the police response include Crisis Intervention Team (CIT) training and various forms of co-response models neither of which have been subjected to comprehensive evaluations, particularly as to cost-efficiency. A new approach is the use of the interRAI Brief Mental Health Screener to enhance police officer ability to identify persons with serious mental disorders. The purpose of the current study is to evaluate the costs and cost efficiency of the police response to mental health calls using the interRAI Brief Mental Health Screener. Method:Secondary data was analyzed from the use of the screener from 2018 to 2020 by police officers in a mid-sized Canadian city. Changes were measured in the overall number of interactions police officers had with persons with mental health disorders, the number of incidents where police officers referred the person to hospital, and the time officers remained in the emergency department. Results:A total of 6,727 assessments were completed with involuntary referrals decreasing by 30%, and voluntary referrals by 34%. The overall time police officers were involved in involuntary referrals decreased from 123 min in 2018 to 113 min in 2020. The average emergency department wait time for voluntary referrals dropped from 41 min in 2018 to 27 min in 2020, while involuntary referrals decreased from 61 min in 2018 to 42 min in 2020. Each averted involuntary referral to the emergency department resulted in a savings of $81, on average during the study period. Conclusion:An analysis of the costs and costs savings associated with the use of the screener demonstrate that it is a worthwhile investment for police services. An additional benefit is its ability to collect mental health statistics that may be useful to police leaders to justify budgets. Future studies should attempt to devise some method of collecting pre-implementation data that would reveal the true costs and cost-efficiency of using the BMHS, which have been shown to be significant in the current study however, undoubtedly are under-estimated.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".