Responding to persons in mental health crisis: A cross-country comparative study of professionals’ perspectives on psychiatric ambulance and street triage models
Bibliographic record
Abstract
People with mental illness can experience mental health crises (MHCs) that manifest in behaviours risky for the affected persons and for others, often resulting in unwanted police encounters and detention. Mobile crisis teams employing the psychiatric ambulance model (PAM) have shown positive effects when responding to MHCs, including diverting patients from police custody. However, the literature contains few reports about PAM. The emerging model of street triage (ST) is more frequently used and better researched. This study explored and compared facilitators and barriers of PAM and ST from the perspective of professionals from different countries. We conducted 12 semi-structured interviews with key PAM stakeholders in Sweden and the Netherlands and ST stakeholders in England, then performed comparative thematic analysis. Participants believed that PAM and ST led to better care for persons in MHC, reducing stigma and use of force. The main facilitators for Swedish participants were that PAM is a specialty with highly experienced and autonomousstaff. For Dutch participants, the more generalized medium-care ambulance led to success. Street triage enhanced overall safety and interagency collaboration. A common barrier was the lack of (emergency) treatment options and funding to meet the high demand for mental health care. Future research should explore collaboration between mobile crisis teams and community care to improve MHC response, and the perspectives of persons with mental illness on mental health emergency response models. Careful assessment is recommended to determine which mental health emergency response model best suits a specific local or national context.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".