A Systematic Review of the Effect of Early Interventions for Psychosis on the Usage of Inpatient Services
Bibliographic record
Abstract
OBJECTIVES: To review and synthesize the currently available research on whether early intervention for psychosis programs reduce the use of inpatient services. METHODS: A systematic review was conducted using keywords searches on PubMed, Embase (Ovid), PsycINFO (ProQuest), Scopus, CINAHL (EBSCO), Social Work Abstracts (EBSCO), Social Science Citations Index (Web of Science), Sociological Abstracts (ProQuest), and Child Development & Adolescent Studies (EBSCO). To be included, studies had to be peer-reviewed publications in English, examining early intervention programs using a variant of assertive community treatment, with a control/comparison group, and reporting inpatient service use outcomes. The primary outcome extracted number hospitalized and total N. Secondary outcome extracted means and standard deviations. Data were pooled using random effects models. Primary outcome was the occurrence of any hospitalization during treatment. A secondary outcome was the average bed-days used during treatment period. RESULTS: Fifteen projects were identified and included in the study. Results of meta-analysis supported the occurrence of a positive effect for intervention for both outcome measures (any hospitalization OR: 0.33; 95% CI 0.18-0.63, bed-days usage SMD: -0.38, 95% CI -0.53 to -0.24). There was significant heterogeneity of effect across the studies. This heterogeneity is due to a handful of studies with unusually positive responses. CONCLUSION: These results suggest that early intervention programs are superior to standard of care, with respect to reducing inpatient service usage. Wider use of these programs may prevent the occurrence of admission for patients experiencing the onset of psychotic symptoms.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".