Psychiatric Rehospitalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Rehospitalization affects quality of life and health system efficiency. Although this outcome is a common quality indicator, there are few applications for linking evaluation to risk mitigation at the person level. This study examined risk factors for rehospitalization to develop an application for supporting care planning based on the interRAI Mental Health (MH), a commonly available assessment system. METHOD: A retrospective analysis was performed of 53,538 psychiatric inpatients assessed with the interRAI MH in Ontario, Canada, between January 2010 and May 2014. The interRAI MH is a clinical system for assessing demographic variables, service utilization, functional status, and clinical needs. Logistic regression models and survival analysis were used to develop the Rehospitalization Clinical Assessment Protocol by predicting 90-day rehospitalization to any inpatient mental health bed. RESULTS: Variables found to significantly predict rehospitalization included 6 or more lifetime hospitalizations (odds ratio [OR] = 1.40), positive symptoms of psychosis (OR = 1.23), a secondary substance use disorder (OR = 1.13), and being at risk of harm to self (OR = 1.11). Using these variables, the Rehospitalization Clinical Assessment Protocol was derived whereby those at level 2 (highest) were 74% more likely to be rehospitalized within 90 days than those at level 0. By 1-year postdischarge, 30% at level 2 and 18% at level 0 were rehospitalized. CONCLUSIONS: The Rehospitalization Clinical Assessment Protocol is an application supporting care planning for targeting risk of rehospitalization whenever a person is assessed with the interRAI MH. Further exploration is needed to understand how the use of this Clinical Assessment Protocol, service processes, and health system structures further mediate or moderate psychiatric rehospitalization risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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