A Meta-Analysis of Police Response Models for Handling People With Mental Illnesses: Cross-Country Evidence on the Effectiveness
Bibliographic record
Abstract
Recent global statistics on mental health showed that the number of people with mental illnesses has dramatically increased in many countries. A gatekeeper to the criminal justice system, police have begun to develop their own police response programs or have adopted renowned models from other countries for handling people with mental illnesses. Although there is a growing body of empirical research that has investigated the effectiveness of police response models for handling the mentally ill (PRMHMI) in various countries, existing systematic reviews or meta-analytic studies have disproportionately focused on findings from studies testing the effects of the crisis intervention team in the United States. Thus, it is still not clear whether PRMHMI can be considered as “evidence-based” models on the international level. To help fill this gap in the literature, the current systematic review and meta-analysis compared the effectiveness of PRMHMI operating in the United States to those operating in other countries including the United Kingdom, Australia, Canada, and Liberia. Results revealed that the effect sizes of PRMHMI were substantially different across countries. This study’s results demonstrate the importance of a national context for designing, implementing, and evaluating PRMHMI.
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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 | 0.038 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.057 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".