Predictors of psychiatric re‐hospitalization in older adults with severe mental illness
Bibliographic record
Abstract
OBJECTIVE: Many patients with severe recurrent mental illness are approaching late life; however, little is known about psychiatric re-hospitalization in this population. Our objective was to identify predictors of psychiatric re-hospitalization. METHODS: This was a retrospective cohort study of all 226 geriatric patients (age ≥65 years) admitted to a tertiary care Canadian inpatient psychiatric unit between 2003 and 2008. The main outcome was psychiatric re-hospitalization in 5-year follow-up post-discharge (e.g. 2008-2013 if a patient had been first admitted in 2008). Multivariate Cox regression analyses were used to identify potential predictors of re-hospitalization. RESULTS: Over 5-year follow-up, 32.3% (73/226) required psychiatric re-hospitalization. Prior lifetime history of psychiatric admission, currently living in a supervised setting and bipolar disorder diagnosis all independently predicted a lower time to psychiatric re-hospitalization (HRs > 2.0, p < 0.05). CONCLUSIONS: The rate of psychiatric re-hospitalization is high in older adults admitted for severe mental illness. Clinicians should be aware of the especially high rates of re-hospitalization in geriatric psychiatric inpatients with bipolar disorder, previous psychiatric admissions, or those living in a supervised setting. Future research could investigate approaches to prevent psychiatric re-hospitalization in these vulnerable sub-populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".