The Judicious Judicial Dispositions Juggle: Characteristics of Police Interventions Involving People with a Mental Illness
Bibliographic record
Abstract
OBJECTIVE: The number of police interventions with people presenting a mental health problem has been increasing during the past 30 years, and police services are becoming increasingly aware of the human resources and skills these interventions require. Our study addresses the characteristics explaining police time used and outcomes of interventions as police officers interact with people with mental illness. METHOD: Using a police service administrative database from a large Canadian city, and an identification algorithm method, police interventions with people with mental illness were identified on 3 randomly selected days in 1 year. A content analysis of intervention logs was carried out to identify characteristics of those interventions: the call initiator, the location, and the final outcome of the intervention. RESULTS: Interventions with people with mental illness represent a small proportion (3%; n = 272) of all police interventions (n = 8485). General linear models show that the type of outcome is the most important factor in estimating the time required by police interventions. Arrests and hospitalizations are the least time-efficient outcomes, consuming 2.0 and 3.2 times, respectively, more time than informal dispositions. A multiple correspondence analysis shows that police interventions can be depicted in 2 dimensions, representing their main roles concerning people with mental illness, namely, to ensure the public safety and to protect the most vulnerable citizens. The more these services are required, the more police time will be required. CONCLUSION: Education and partnerships between police services and mental health services are essential to a proper management of outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".