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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".