A Case-Control Study of Factors Associated With Multiple Psychiatric Readmissions
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: This case-control study explored factors associated with multiple psychiatric admissions, focusing on service-related and individual-level factors. METHODS: The case group consisted of 307 adults admitted to either of two public psychiatric hospitals in southern Brazil during a 12-month period; they had had three or more psychiatric admissions in the two years before the current admission. To account for the recurrent nature of psychiatric admissions, a concurrent case-control design was adopted, which allowed patients in the case group to return at discharge to the population at risk of readmission. The control group consisted of individuals who had their first inpatient readmission in 2006 (N=354). A hierarchical model with four levels was used for data analysis. RESULTS: Individuals who had been referred to community psychosocial support units after their most recent discharge had about 20% lower odds of multiple readmissions than those referred to usual outpatient care. Those who lived closer to the hospital (residents of the same city) were more likely to have multiple readmissions. The adjusted multivariate hierarchical analysis revealed that a diagnosis of schizophrenia or psychotic symptoms was associated with multiple readmissions, as were younger age at first admission and a greater number of previous admissions. CONCLUSIONS: The study suggests that community psychosocial support services play a strong role in preventing multiple psychiatric admissions. Further research is needed to identify the specific components of these services that reduce readmission and to analyze their cost-effectiveness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it