Supportive Text Messaging and Peer Support for Patients in the 6 Months Following Discharge from a Psychiatric Admission: Mental Health Outcomes from a Cluster-Randomized Controlled Trial
Bibliographic record
Abstract
Background/Objective: The transition from psychiatric inpatient care to community settings poses risks of relapse, rehospitalization, and poor well-being. This study examined changes in anxiety, depression, suicidal ideation, sleep issues, and well-being over six months post-discharge and assessed the effectiveness of supportive text messaging (Text4Support) alone and with peer support, compared to treatment as usual (TAU). Methods: A pragmatic stepped-wedge cluster-randomized trial included 1098 participants discharged from psychiatric units across Alberta, Canada. Participants were allocated to the TAU, Supportive Text Messaging (SMS), or SMS plus peer support (SMS+PS) group. Outcomes were measured using GAD-7, PHQ-9, and WHO-5 at baseline and at six weeks, three months, and six months post-discharge. ANCOVA compared outcomes across groups at each time point, controlling for baseline values. Results: Follow-up completion declined (20% at six weeks, 16% at three months, and 15% at six months). No group differences emerged for anxiety, depression, suicidal ideation, or sleep issues at six weeks or three months. Well-being was significantly higher in the SMS group at six weeks (η2 = 0.10). At six months, between-group differences appeared for anxiety and depression, though post hoc tests showed no pairwise differences. Conclusions: Supportive text messaging may improve well-being shortly after discharge and holds promise as a low-intensity transitional care strategy, though findings are limited by low follow-up.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".