Homelessness at discharge and its impact on psychiatric readmission and physician follow-up: a population-based cohort study
Bibliographic record
Abstract
AIMS: A significant proportion of adults who are admitted to psychiatric hospitals are homeless, yet little is known about their outcomes after a psychiatric hospitalisation discharge. The aim of this study was to assess the impact of being homeless at the time of psychiatric hospitalisation discharge on psychiatric hospital readmission, mental health-related emergency department (ED) visits and physician-based outpatient care. METHODS: This was a population-based cohort study using health administrative databases. All patients discharged from a psychiatric hospitalisation in Ontario, Canada, between 1 April 2011 and 31 March 2014 (N = 91 028) were included and categorised as homeless or non-homeless at the time of discharge. Psychiatric hospitalisation readmission rates, mental health-related ED visits and physician-based outpatient care were measured within 30 days following hospital discharge. RESULTS: There were 2052 (2.3%) adults identified as homeless at discharge. Homeless individuals at discharge were significantly more likely to have a readmission within 30 days following discharge (17.1 v. 9.8%; aHR = 1.43 (95% CI 1.26-1.63)) and to have an ED visit (27.2 v. 11.6%; aHR = 1.87 (95% CI 1.68-2.0)). Homeless individuals were also over 50% less likely to have a psychiatrist visit (aHR = 0.46 (95% CI 0.40-0.53)). CONCLUSION: Homeless adults are at higher risk of readmission and ED visits following discharge. They are also much less likely to receive post-discharge physician care. Efforts to improve access to services for this vulnerable population are required to reduce acute care service use and improve care continuity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".