Mental health triage in the ER: a qualitative study
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The London Health Sciences Centre found that its emergency room (ER) mental health services were affected by people presenting with problems that did not require psychiatric intervention. Consequently, a second triage using a crisis worker (CW) was introduced in the ER to identify those persons with mental illness (PMI) who presented for social stressors related to housing, finances and legal issues. A qualitative, process evaluation study was conducted to capture experiences and perceptions of the new triage and CW. METHOD: Qualitative input was obtained from a broad range of stakeholders in three waves of data-gathering over a 25-month period. This method allowed corroboration of findings from informants with varying interests and backgrounds. The data were collected through interviews, focus groups and surveys. The NUD-ist Qualitative Data Analysis Software Program was used to conduct content analyses. RESULTS: Many PMI seeking ER mental health services are presenting with problems related to social stressors and being referred by the second triage to the CW. The introduction of the second triage CW has had a positive effect on ER functioning, the workload of ER staff and the experience of persons presenting at ER. CONCLUSIONS: A defined triage process coupled with the use of psychiatric nursing staff may be applicable to ERs within general hospital settings to improve ER functioning, focus support for PMI and further integrate ERs within the community mental health model.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".