Predictors of Psychiatric Emergency Department Visits Following Inpatient Discharge: Secondary Analysis of a Stepped-Wedge Cluster Randomized Trial
Bibliographic record
Abstract
BACKGROUND: The period following discharge from psychiatric inpatient care represents a critical transition phase marked by heightened vulnerability to relapse, including increased risks of emergency department (ED) utilization. Understanding the risk factors for ED utilization after hospital discharge will help identify individuals who should be targeted for enhanced follow-up care in the community. OBJECTIVE: This study aimed to examine the sociodemographic and clinical factors associated with psychiatric ED utilization within six months of discharge from inpatient psychiatric care among individuals assigned to different post-discharge interventions. The goal is to identify high-risk groups to inform targeted follow-up strategies and enhance transitional care planning. METHODS: This study analyzed secondary data from a pragmatic stepped-wedge cluster-randomized trial which recruited patients across ten healthcare sites in Alberta, Canada, from March 2022 to February 2024. For the primary study, a total of 1,098 psychiatric inpatients were allocated to one of three post-discharge conditions: treatment as usual (TAU), supportive text messaging (SMS), or supportive text messaging plus peer support (SMS+PS). Sociodemographic and clinical data were collected at discharge. ED visits 6-months post-discharge were recorded. Chi-square tests identified variables associated with ED utilization. Significant predictors were entered into a logistic regression model to determine adjusted odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Of the 1,098 participants, demographic and clinical variables were examined for association with mental health ED visits at 6-months post discharge. Univariate analysis identified six significant predictors: age, ethnicity, relationship status, employment, housing status, and prior ED use. Logistic regression analysis identified several predictors of mental health ED visits 6-months post-discharge. Compared to participants under 25, those aged 26-40 was less likely to revisit the ED (OR = 0.66, 95% CI: 0.46-0.95), as were those over 40 (OR = 0.58, 95% CI: 0.37-0.92). Individuals identifying as mixed/other ethnicity were less likely than Caucasians to return to the ED (OR = 0.52, 95% CI: 0.28-0.96). Unemployed participants had higher odds of ED use than those employed (OR = 1.66, 95% CI: 1.18-2.34). Prior ED attendance was the strongest predictor (OR = 2.45, 95% CI: 1.03-5.80). Housing status showed varied but non-significant effects. CONCLUSIONS: This study highlights key demographic and clinical factors influencing psychiatric ED use following inpatient discharge. The findings emphasize the importance of targeted transitional care interventions, particularly for high-risk groups such as younger, unemployed, and previously ED-utilizing individuals, and support the integration of scalable approaches like SMS and peer support into discharge planning. CLINICALTRIAL: clinicaltrials.gov, NCT05133726. Registered 24 November 2021.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".