Police Encounters Involving Citizens With Mental Illness: Use of Resources and Outcomes
Bibliographic record
Abstract
OBJECTIVE: Few studies have addressed use of resources in police interventions involving individuals with mental illness. The time police officers spend on interventions is a straightforward measure with significant administrative weight, given that it addresses human resource allocation. This study compared the characteristics of police interventions involving individuals with mental illness and a control sample of individuals without mental illness. METHODS: A total of 6,128 police interventions in Montreal, Québec, were analyzed by using a retrospective analysis of police intervention logs from three days in 2006. Interventions involving citizens with (N=272) and without (N=5,856) mental illness were compared by reason for the intervention, the use of arrest, and the use of police resources. RESULTS: Police interventions involving individuals with mental illness were less likely than those involving individuals without mental illness to be related to more severe offenses. However, interventions for minor offenses were more likely to lead to arrest when they involved citizens with mental illness. Interventions for reasons of equal severity were twice as likely to lead to arrest if the citizen involved had a mental illness. After controlling for the use of arrest and the severity of the situation, the analysis showed that police interventions involving individuals with mental illness used 87% more resources than interventions involving individuals without mental illness. CONCLUSIONS: Future studies using administrative police data sets could investigate the use of resources and division of costs involved in new programs or partnerships to better address the interface of criminal justice and mental health care.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".