Depression, anxiety, and poor well-being at discharge from psychiatric hospitals: prevalence and risk factors
Bibliographic record
Abstract
Background: Being ready for discharge is vital to successful hospital-to-home transitions. For many patients, however, the transition from psychiatric hospital care to outpatient care can be challenging. An in-depth understanding of the mental health conditions of patients at discharge is crucial and instructive for recovery research. Objective: The purpose of this study was to determine the prevalence and risk factors of depression, anxiety, and poor well-being symptoms among patients who are about to be discharged from psychiatric units in Alberta, Canada. Our aim was to help determine the prevalence of anxiety, depression, and overall well-being among the general psychiatric inpatient population in Alberta before discharge and the potential factors which may influence this. Methods: This epidemiological study used a cross-sectional quantitative survey from March 8, 2022, to November 5, 2023, to assess depression, anxiety, and well-being. Participants were invited to complete an online questionnaire that contained demographics, clinical information, and responses to the PHQ-9, GAD-7, and WHO-5 questionnaires. SPSS version 25 was used to analyze the data. Descriptive, univariate, and multivariate regression analyses were employed. Result: The study found that the prevalence of likely depression, anxiety, and poor well-being among patients about to be discharged was 37.1%, 56.4%, and 48.3%, respectively. Based on a logistic regression model, there was a statistically significant association between anxiety, depression, and poor well-being diagnoses and multiple socio-demographic and clinical factors such as ethnicity, primary mental health diagnoses, education level, housing status, depression, anxiety, and well-being at baseline. Conclusion: Mental health assessment at discharge is a critical step in the recovery and transition of care. There is still a need for further research to identify the underlying causes and robust predictors of mental health symptoms in patients about to be discharged and to provide appropriate interventions and supportive resources both before and following discharge. Future research utilizing these findings may help identify key opportunities to improve outcomes for patients after discharge.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".