Thirty-Day and 5-Year Readmissions following First Psychiatric Hospitalization: A System-Level Study of Ontario’s Psychiatric Care
Bibliographic record
Abstract
OBJECTIVE: Analyses of representative, system-level data to examine trends in short- and longer-term readmission rates for psychiatric illnesses are largely absent. The objective of this article is to examine key trends and variables with implications for inpatient care as indicated by 30-day readmission and outpatient care as reflected by readmission within 5 years. METHODS: Using OMHRS data from 2005 to 2015, patients who had their first inpatient admission were followed for 5 years to examine their subsequent 30-day and overall admission rates stratified by discharge time and diagnosis. RESULTS: The study cohort consisted of 42,280 patients. The 30-day and 5-year readmission rates for the entire cohort were 7.2% and 35.1%, respectively. Using a time course analysis of readmission for discharges in different years, both 30-day readmission and 5-year readmission rates decreased in a linear manner from 2005 to 2010, primarily because of readmission patterns for patients diagnosed with mood disorders and schizophrenia/other psychotic disorders. It was also evident that both demographic considerations such as age and gender and variables reflective of social determinants such as education level and employment were predictive of rehospitalization risk. CONCLUSIONS: The trends of decreasing readmission rates may be reflective of improvements in the quality of hospital and community-based outpatient care. Such system-level indicators warrant tracking and may inform more effective tertiary prevention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".