Emergency Department Use among Patients with Mental Health Problems: Profiles, Correlates, and Outcomes
Bibliographic record
Abstract
Patients with mental health (MH) problems are known to use emergency departments (EDs) frequently. This study identified profiles of ED users and associated these profiles with patient characteristics and outpatient service use, and with subsequent adverse outcomes. A 5-year cohort of 11,682 ED users was investigated (2012-2017), using Quebec (Canada) administrative databases. ED user profiles were identified through latent class analysis, and multinomial logistic regression used to associate patients' characteristics and their outpatient service use. Cox regressions were conducted to assess adverse outcomes 12 months after the last ED use. Four ED user profiles were identified: "Patients mostly using EDs for accessing MH services" (Profile 1, incident MDs); "Repeat ED users" (Profile 2); "High ED users" (Profile 3); "Very high and recurrent high ED users" (Profile 4). Profile 4 and 3 patients exhibited the highest ED use along with severe conditions yet received the most outpatient care. The risk of hospitalization and death was higher in these profiles. Their frequent ED use and adverse outcomes might stem from unmet needs and suboptimal care. Assertive community treatments and intensive case management could be recommended for Profiles 4 and 3, and more extensive team-based GP care for Profiles 2 and 1.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".