A Digital Dialectical Behaviour Therapy Intervention for Acute Suicidality in Psychiatric Inpatients: A Feasibility Randomised Controlled Study: Intervention numérique en thérapie comportementale dialectique en cas de suicidabilité aiguë de patients hospitalisés en psychiatrie : Étude de faisabilité contrôlée à répartition aléatoire
Bibliographic record
Abstract
OBJECTIVE: To evaluate the feasibility and preliminary efficacy of a digital dialectical behaviour therapy (d-DBT) skills intervention in suicidal psychiatric inpatients. METHODS: A parallel arm, assessor-blinded, randomized controlled trial (RCT) was conducted to compare d-DBT to standard care among psychiatric inpatients. Participants included adults admitted for suicidality (i.e., suicidal ideation or suicide attempt). The intervention group received a d-DBT intervention encompassing 5 online modules completed over 5 to 10 days, covering mindfulness, emotion regulation, and distress tolerance skills. Participants received an initial orientation but no formal therapy sessions. Daily check-ins were available for technical-related queries. Feasibility outcomes included recruitment, adherence (≥3 modules completed), retention, and acceptability (client satisfaction questionnaire-8). Efficacy outcomes included suicidality (Columbia-Suicide Severity Rating Scale [C-SSRS] total score), psychological distress (K10), emotion regulation (Difficulties in Emotion Regulation Scale-16 [DERS-16]), and clinical global impression (CGI). Linear regression models analysed group differences. RESULTS: A total of 65 participants were recruited, of which 42 were randomized, with high d-DBT adherence rates in the intervention arm (75%). The d-DBT intervention demonstrated significant reductions in C-SSRS scores (Cohen's -1.0) compared to standard of care. No significant group differences were observed in K10, DERS-16, or CGI. High acceptability and satisfaction were reported among participants randomized to d-DBT. Challenges and limitations included maintaining follow-up postdischarge and the small sample size. CONCLUSION: d-DBT is feasible to implement through an RCT and may reduce suicidality and improve mental health among psychiatric inpatients. The study highlights the importance of developing accessible, evidence-based interventions for this population. Future research should focus on long-term efficacy and expanding the intervention's appeal and accessibility.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".