Profiles, Correlates and Outcomes Among Patients Experiencing an Onset of Mental Disorder Based on Outpatient Care Received Following Index Emergency Department Visits
Bibliographic record
Abstract
OBJECTIVE: This 5-year longitudinal study evaluated patients with an onset of mental disorder (MD) following index emergency department (ED) visits, in terms of (1) patient profiles based on 12-month outpatient follow-up care received, (2) sociodemographic and clinical correlates, and (3) adverse health outcomes for the subsequent 2 years. METHODS: Data from administrative databases were collected for 2541 patients with an onset of MD, following discharge from Quebec ED. Latent class analysis was performed to identify patient profiles based on the adequacy of follow-up care after ED discharge. Bivariate analyses examined associations between class membership and sociodemographic and clinical correlates, high ED use (3 + visits/yearly), hospitalizations, and suicidal behaviors. RESULTS: Five classes of patients were identified. Class 1, the smallest, labeled "patient psychiatrist only," included mainly young patients with serious MD. Classes 2 and 3, roughly 20%, were labeled "high use of patient general practitioner (GP) and psychiatrist" and "low use of patient GP and psychiatrist," respectively. Both included patients with complex MD, but Class 2 had more women and older patients with chronic physical illnesses. The 2 largest classes were labeled "no usual patient service provider" (Class 5) and "patient GP only" (Class 4). Class 5 included more younger men with substance-related disorders, while Class 4 had the older patients living in rural areas, many with common MD and chronic physical illnesses. Class 3 patients had the poorest outcomes, followed by Classes 1 and 2, while Classes 4 and 5 had the best outcomes. CONCLUSIONS: Results revealed that nearly 40% of patients experiencing an onset of MD received little or no outpatient care following ED discharge. Higher severity or complexity of MD and, to a lesser extent, no or low GP follow-up may explain these adverse outcomes. More adequate, continuous care, including collaborative care, is needed for these vulnerable, high-needs patients.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".