Support After Discharge: The Role of Digital and Peer-Based Interventions in Reducing Psychiatric Symptoms and Emergency Service Utilization
Bibliographic record
Abstract
Background: The period immediately following discharge from psychiatric inpatient care is a critical transitional phase often marked by relapse, suicidal ideation, and difficulties in re-engaging with community-based care. Scalable post-discharge interventions are increasingly sought to support recovery and continuity of care. Supportive digital approaches, such as text messaging and peer support (e.g., Text4Support), have demonstrated feasibility, but further research is needed to evaluate their impact on patient outcomes and emergency service use. This thesis investigates mental health trajectories following psychiatric discharge and evaluates supportive interventions on key outcomes. Objective: This thesis aimed to examine the role of digital and peer-based interventions in supporting individuals after discharge from acute psychiatric care through three objectives: 1. To conduct a scoping review mapping global prevalence and characteristics of psychiatric emergency department (ED) readmissions and synthesizing interventions aimed at reducing repeat psychiatric ED use. 2. To examine post-discharge trajectories of depression, anxiety, suicidal ideation, well-being, and sleep disturbances at six weeks, three months, and six months after psychiatric inpatient discharge in Alberta, Canada. 3. To assess the effectiveness of daily supportive text messaging (Text4Support), with or without peer support, in improving outcomes and reducing psychiatric ED visits, while identifying sociodemographic and clinical predictors of relapse and service use. Methods: Scoping reviews followed PRISMA-ScR guidelines. PubMed, PsycINFO, MEDLINE, JSTOR, Scopus, and Web of Science were searched for studies evaluating interventions to reduce repeat ED visits among individuals with mental health conditions. Two reviewers screened and extracted data on intervention types, effectiveness, targeted conditions, and patient characteristics. The six empirical studies in this thesis were epidemiological analyses within a pragmatic stepped-wedge cluster-randomized trial across ten acute psychiatric units in Alberta, beginning in March 2022. The trial evaluated supportive text messaging, alone or with peer support, for patients discharged from inpatient care. Adults (18+) completed baseline REDCap surveys on sociodemographic and clinical factors, plus validated scales for anxiety (GAD-7), depression (PHQ-9), well-being (WHO-5), suicidal ideation, and sleep disturbances, with follow-ups at six weeks, three months, and six months. “Likely depression” and “likely anxiety” refer to validated cut-offs (PHQ-9, GAD-7) rather than diagnoses, consistent with research conventions. Analyses employed descriptive statistics, chi-square tests, and regression models using SPSS v25. Ethical approval was granted by the University of Alberta Health Research Ethics Board. Results: Scoping review: Twenty-six studies were identified, evaluating interventions such as the High Alert Program, Patient-Centered Medical Home, Primary Behavioral Health Care Integration, and Collaborative Care. Most (n=23) were North American, with others from Europe and Australia. Sixteen targeted general mental health, while others addressed substance use, schizophrenia, anxiety, or depression. Effective interventions emphasized multidisciplinary care, evidence-based strategies, and case management, with some tailored to youth or substance use populations. Most demonstrated positive effects on reducing ED use. Clinical outcomes and predictors: Response rates declined over time, with 218 participants (20%) completing six-week follow-up, 176 (16%) at three months, and 168 (15%) at six months, yielding 1,098 complete cases. Of these, 439 (40%) were in treatment-as-usual (TAU), 541 (49.3%) in the SMS group, and 118 (10.7%) in SMS plus peer support. Baseline symptom severity strongly predicted outcomes. While six-week changes were minimal, significant improvements in anxiety and depression emerged by six months among participants receiving interventions. Age, ethnicity, employment, diagnosis type, and initial symptom levels consistently predicted poorer outcomes. Conclusion: This study underscores the persistent challenges after psychiatric discharge, including anxiety, depression, suicidal ideation, and sleep issues. Demographic and clinical factors were key predictors of outcomes. Supportive interventions, particularly Text4Support, reduced symptoms over time, with sustained benefits. Given its scalability and cost-effectiveness, Text4Support offers practical potential for enhancing transitional care. Health systems should integrate such tools into discharge planning to support recovery and reduce ED utilization. Future work should tailor digital interventions to improve engagement, address individual needs, and ensure continuity of care.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".