Adaptation and evaluation of a digital dialectical behaviour therapy for youth at clinical high risk for psychosis: A protocol for a feasibility randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Youth at clinical high risk (CHR) for psychosis often experience emotional dysregulation, psychiatric symptoms, substance use, suicidality, and functional impairment. Dialectical behaviour therapy (DBT) is an evidence-based intervention that improves emotion regulation, clinical outcomes, and functioning across psychiatric populations. Digital adaptations (d-DBT) may enhance accessibility and engagement for CHR youth, but acceptability and potential benefits in this group are unknown. OBJECTIVE: To adapt d-DBT for CHR youth and evaluate the acceptability of delivering it to this population, as well as the feasibility of a larger-scale clinical trial. METHODS: This mixed-methods clinical trial has two phases. In Phase 1, d-DBT will be adapted for CHR youth in collaboration with a lived-experience youth advisory group. In Phase 2, an assessor-masked randomized controlled trial will compare d-DBT (n = 30) with treatment as usual (n = 30). The intervention consists of eight weekly modules, with primary outcomes assessing acceptability, usability, and trial feasibility. Secondary outcomes include changes in emotional dysregulation, psychiatric symptoms, substance use, suicidality, and functioning. CONCLUSIONS: We anticipate that d-DBT will be acceptable to CHR youth and that conducting a larger trial will be feasible. Preliminary findings may demonstrate improvements in emotion regulation, psychiatric symptoms, suicidality, and functioning. Results will guide further refinement of the intervention and inform the design of a confirmatory clinical trial. TRIAL REGISTRATION: ClinicalTrials.gov #NCT06928935.
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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 | 0.071 | 0.052 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".