Effectiveness of Text Messages and Text Messages Plus Peer Support on Psychiatric Readmission and Length of Stay: Outcomes From a Quantitative Stepped-Wedge Cluster Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Mental health recovery typically continues after patients leave the hospital. However, hospital readmission in the 12 months after discharge is common and costly. OBJECTIVE: This study aimed to examine the effectiveness of supportive text messaging (hereinafter "SMS") and SMS with or without peer support service on hospital readmission and length of stay after discharge from inpatient psychiatric care. METHODS: A stepped-wedge cluster randomized trial was used to examine differences in the changes in the mean number of admissions and the mean duration of total length of stay in days, for patients discharged from psychiatric inpatient care, at 6 and 12 months pre- and post index admissions, for 2 intervention periods compared to a control period of treatment as usual. RESULTS: Overall, 1070 participants were assigned to 1 of 3 study arms: SMS (n=302), SMS with or without peer support service (n=342), or treatment as usual (n=426). Compared to treatment as usual, SMS with or without peer support service reduced hospital readmissions 6 months pre- and post index admission by an average of 0.26 admissions, and SMS alone reduced inpatient length of stays 6 months pre- and post index admission by an average of 7.28 days. CONCLUSIONS: Our results demonstrate that simple, low-cost digital tools-either by themselves or paired with peer support-can help close gaps in postdischarge care. We anticipate that these findings may inform future service delivery models and policy development aimed at enhancing postdischarge mental health support. By supporting smoother transitions and reducing future hospital use, such approaches may offer a scalable way to build more sustainable and person-centered mental health systems. TRIAL REGISTRATION: ClinicalTrials.gov NCT05133726; https://clinicaltrials.gov/study/NCT05133726.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".