Wait times in the emergency department for patients with mental illness
Bibliographic record
Abstract
BACKGROUND: It has been suggested that patients with mental illness wait longer for care than other patients in the emergency department. We determined wait times for patients with and without mental health diagnoses during crowded and noncrowded periods in the emergency department. METHODS: We conducted a population-based retrospective cohort analysis of adults seen in 155 emergency departments in Ontario between April 2007 and March 2009. We compared wait times and triage scores for patients with mental illness to those for all other patients who presented to the emergency department during the study period. RESULTS: The patients with mental illness (n = 51 381) received higher priority triage scores than other patients, regardless of crowding. The time to assessment by a physician was longer overall for patients with mental illness than for other patients (median 82, interquartile range [IQR] 41-147 min v. median 75 [IQR 36-140] min; p < 0.001). The median time from the decision to admit the patient to hospital to ward transfer was markedly shorter for patients with mental illness than for other patients (median 74 [IQR 15-215] min v. median 152 [IQR 45-605] min; p < 0.001). After adjustment for other variables, patients with mental illness waited 10 minutes longer to see a physician compared with other patients during noncrowded periods (95% confidence interval [CI] 8 to 11), but they waited significantly less time than other patients as crowding increased (mild crowding: -14 [95% CI -12 to -15] min; moderate crowding: -38 [95% CI -35 to -42] min; severe crowding: -48 [95% CI -39 to -56] min; p < 0.001). INTERPRETATION: Patients with mental illness were triaged appropriately in Ontario's emergency departments. These patients waited less time than other patients to see a physician under crowded conditions and only slightly longer under noncrowded conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".