Support After Discharge: The Role of Digital and Peer-Based Interventions in Reducing Psychiatric Symptoms and Emergency Service Utilization
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».