Exploring the factors associated with non-urgent emergency department utilisation for mental health care
Bibliographic record
Abstract
BACKGROUND: Emergency department visits for mental health care are on the rise across North America. Patients with mental illness are at an increased risk for frequent and non-urgent emergency department visitation. AIMS: The purpose of this study was to examine the independent predictors of non-urgent emergency department use for mental health care. METHODS: A secondary data analysis was conducted with archived data provided by the Erie St. Clair Local Health Integration Network in Ontario. RESULTS: A total of 13,114 mental health-related emergency department visits were analysed using logistic regression with generalised estimating equations modelling. The findings suggest the following characteristics are predictive of non-urgent emergency department use for mental health care: age, season, time of day, access to primary health care, mode of arrival, hospital type, referral source and patient diagnosis. CONCLUSIONS: The findings of this study can be utilised to assist clinicians and policy makers in identifying and managing patients using the emergency department for non-urgent mental health care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".