Processes of co-response crisis mental health models: a scoping review
Bibliographic record
Abstract
Objective Police services assume the role of first responders for mental health crisis situations, providing no mental health support risking police-inflicted harm to the person in crisis. Advances in mental health and police reform have produced alternative response models like co-response that includes both police officers and trained mental health service providers. This scoping review synthesises knowledge to present key processes that facilitate co-response models.Method Covidence software was used by two independent reviewers to search eleven databases with terms including ‘co-response’, ‘police partnerships’, and ‘crisis response team’. These reviewers screened 2690 titles and abstracts and 109 full-texts to include 76 articles. Nine independent coders employed thematic content analysis.Results Three themes and seven sub-themes were determined as key processes of co-response. Decriminalising mental health involves re-evaluating the role of police and reducing reliance on police in crisis mental health response. Interprofessional collaboration features partnerships between existing services, unifying strategies, and establishing multidisciplinary teams. A piloting phase permits monitoring and evaluation and highlights the diversity of co-response models.Discussion Co-response crisis mental health models are increasingly common, heterogeneous in design and operation, and built upon existing infrastructure. Future inquiries could include trauma-informed care and the evaluation of civilian-led models..
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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 | 0.025 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".