Effectiveness of Interventions After Psychiatric Hospitalization: A Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVE: Reducing psychiatric readmission rate is an important goal because psychiatric hospitalization ranks among the highest hospitalization rates. Although individual posthospitalization interventions can reduce psychiatric readmissions, the effectiveness of these interventions remains inconclusive. The authors evaluated the effectiveness of posthospitalization interventions in reducing psychiatric readmissions and improving psychiatric outcomes. METHODS: Ovid MEDLINE, Web of Science, Scopus, and CINAHL databases were searched for articles published between January 2000 and December 2024. Randomized controlled trials (RCTs) examining interventions after psychiatric hospitalization were identified. Data screening and extraction were conducted by at least two reviewers. A random-effects meta-analysis was conducted, as well as multiple stratified subgroup analyses, and pooled relative risks were estimated. RESULTS: Twelve RCT studies with 3,663 participants were included, of which only two (17%) had a high risk of bias and were accounted for in the sensitivity analysis. Posthospitalization intervention categories were case management, care coordination, care plan, and peer support. Compared with standard care, the effect of posthospitalization interventions on psychiatric readmissions yielded a pooled risk ratio of 0.96 (95% CI=0.86-1.08, p=0.52), suggesting no evidence of a statistically significant reduction in readmission rates. However, after subgroup analyses, effectiveness was found to be associated with length of intervention. Improvement in psychiatric outcomes varied, and no common improvement was found among studies. CONCLUSIONS: A one-size-fits-all approach may not be effective in reducing psychiatric readmissions, supporting more targeted approaches and posthospitalization interventions that consider patient-specific profiles and needs, engagement in treatment, and availability of services. This study provides meaningful information to fill the knowledge gap on the effectiveness of posthospitalization interventions in reducing psychiatric readmissions.
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.
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 | 0.046 | 0.102 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.031 | 0.068 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".