Investigating characteristics of patients with mental disorders to predict out-patient physician follow-up within 30 days of emergency department discharge
Bibliographic record
Abstract
BACKGROUND: Prompt follow-up at emergency department discharge is a key indicator of healthcare quality and patient recovery. To improve services, better knowledge of predictors for out-patient physician follow-up within 30 days after discharge is needed. AIMS: We investigated clinical and sociodemographic characteristics and service use to predict patients with mental disorders with or without physician follow-up after emergency department use. METHOD: This study used data extracted from clinical administrative databases for 9514 patients who attended an emergency department in Quebec (Canada) in 2014-2015 (index visit) for mental health reasons. Patient clinical and sociodemographic characteristics from 2012-2013 to 2014-2015, and service use 12 months before the index visit, were investigated as predictors for patients with or without prompt follow-up, using hierarchical logistic regression. RESULTS: Two-thirds of patients did not receive prompt physician follow-up. Patients with level 1-2 illness acuity at emergency department triage (needing immediate or urgent care); those with adjustment or bipolar disorders, but without alcohol-related disorders (clinical characteristics); and patients with higher continuity of physician care, more psychosocial interventions in community healthcare centres and prior hospital admission (service use characteristics) were more likely to receive prompt out-patient follow-up. CONCLUSIONS: Access to medical care was poor, considering the high needs of this population. The role of the emergency department as a gateway for accessing out-patient care may be strengthened by strategies like screening, brief intervention including motivational treatments, brief case management offered by emergency department staff, timely referral to services and better post-discharge planning. Collaborative care for patients attending emergency departments should also be improved.
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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 | 0.005 |
| 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 | 0.000 |
| 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".