Mental health emergencies attended by ambulances in the United Kingdom and the implications for health service delivery: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: In the context of increasing demand for ambulance services, emergency mental health cases are among the most difficult for ambulance clinicians to attend, partly because the cases often involve referring patients to other services. We describe the characteristics of mental health emergencies in the East Midlands region of the United Kingdom. We explore the association between 999 (i.e. emergency) call records, the clinical impressions of ambulance clinicians attending emergencies and the outcomes of ambulance attendance. We consider the implications of our results for optimizing patient care and ambulance service delivery. METHODS: We conducted a retrospective observational study of records of all patients experiencing mental health emergencies attended by ambulances between 1 January 2018 and 31 July 2020. The records comprised details of 103,801 '999' calls (Dispatch), the preliminary diagnoses by ambulance clinicians on-scene (Primary Clinical Impression) and the outcomes of ambulance attendance for patients (Outcome). RESULTS: < 0.01). Dispatch was a poor predictor of Primary Clinical impression. The most common predictors of Outcome care pathways other than 'Treated and transported' were records of respiratory conditions at Dispatch and anxiety reported by clinicians on-scene. CONCLUSIONS: Drawing on the expertise of mental health specialists may help '999' dispatchers distinguish between physical and mental health emergencies and refer patients to appropriate services earlier in the response cycle. Further investigation is needed to determine if training Dispatch operatives for early triage and referral can be appropriately managed without compromising patient safety.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".