Risk factors of hospitalization for any medical condition among patients with prior emergency department visits for mental health conditions
Bibliographic record
Abstract
BACKGROUND: This longitudinal study identified risk factors for frequency of hospitalization among patients with any medical condition who had previously visited one of six Quebec (Canada) emergency departments (ED) at least once for mental health (MH) conditions as the primary diagnosis. METHODS: Records of n = 11,367 patients were investigated using administrative databanks (2012-13/2014-15). Hospitalization rates in the 12 months after a first ED visit in 2014-15 were categorized as no hospitalizations (0 times), moderate hospitalizations (1-2 times), and frequent hospitalizations (3+ times). Based on the Andersen Behavioral Model, data on risk factors were gathered for the 2 years prior to the first visit in 2014-15, and were identified as predisposing, enabling or needs factors. They were tested using a hierarchical multinomial logistic regression according to the three groups of hospitalization rate. RESULTS: Enabling factors accounted for the largest percentage of total variance explained in the study model, followed by needs and predisposing factors. Co-occurring mental disorders (MD)/substance-related disorders (SRD), alcohol-related disorders, depressive disorders, frequency of consultations with outpatient psychiatrists, prior ED visits for any medical condition and number of physicians consulted in specialized care, were risk factors for both moderate and frequent hospitalizations. Schizophrenia spectrum and other psychotic disorders, bipolar disorders, and age (except 12-17 years) were risk factors for moderate hospitalizations, while higher numbers (4+) of overall interventions in local community health service centers were a risk factor for frequent hospitalizations only. Patients with personality disorders, drug-related disorders, suicidal behaviors, and those who visited a psychiatric ED integrated with a general ED in a separate site, or who visited a general ED without psychiatric services were also less likely to be hospitalized. Less urgent and non-urgent illness acuity prevented moderate hospitalizations only. CONCLUSIONS: Patients with severe and complex health conditions, and higher numbers of both prior outpatient psychiatrist consultations and ED visits for medical conditions had more moderate and frequent hospitalizations as compared with non-hospitalized patients. Patients at risk for frequent hospitalizations were more vulnerable overall and had important biopsychosocial problems. Improved primary care and integrated outpatient services may prevent post-ED hospitalization.
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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 | 0.000 |
| 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".