Urgent Outpatient Care Following Mental Health ED Visits: A Population-Based Study
Bibliographic record
Abstract
Objective: Follow-up after psychiatric emergency department (ED) contact is key to optimizing outcomes for vulnerable patients. We aimed to quantify the likelihood of receiving outpatient mental health care after psychiatric ED visits in a population-level sample. Methods: Among individuals who presented for a psychiatric ED visit in Ontario, Canada (2010–2012) and were not admitted to hospital (N=143,662), the authors estimated the likelihood of outpatient physician mental health care within 14 days post-ED visit and compared this across presenting diagnoses. Results: About 40.2% (N=57,797) had a follow-up mental health visit within 14 days post-ED. Follow-up was lower among individuals presenting with substance use disorders (25.2%) than among those presenting with disorders not primarily related to substance use (44.5%) (χ2=3,784.7, df=1, p<0.001). Follow-up differed among those presenting with schizophrenia (46.4%), bipolar disorder (56.1%), and major depressive disorder (51.1%) (χ2=61.7, df=2, p<0.001). Conclusions: Post-ED outpatient mental health follow-up is low. Systemwide coordination is needed to connect these high-acuity patients with care, especially those with presentations related to substance use.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
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".