Predictors of Treatment Outcome and Engagement in Self-Guided Internet-Delivered Dialectical Behavior Therapy for Substance Use Disorders
Bibliographic record
Abstract
Background: Substance use disorders (SUD) are debilitating conditions that frequently co-occur with other mental disorders. Internet-delivered dialectical behavior therapy (iDBT) skills training may be promising for SUD; however, little research has examined predictors of engagement and treatment outcome. Methods: This is a secondary, exploratory analysis of a randomized, waitlist-controlled trial of self-guided iDBT for SUD. Participants (N = 72) were allocated to an immediate or delayed arm, with the latter receiving access after 4 weeks. All participants were followed for 12 weeks total. The primary treatment outcome was SUD severity, and secondary outcomes included SUD diagnosis and functional disability. Engagement was assessed as the number of hours and unique days spent on iDBT, as well as inactivity after 4 weeks. Multilevel modeling and standard regression approaches were used to explore associations between predictors and treatment outcome or engagement. Results: Immediate arm membership, greater pretreatment SUD severity, and fewer SUD diagnoses were associated with greater reductions SUD severity. Greater expectancy of change and greater pretreatment disability were associated with greater reductions in functional disability. Spending at least 1 hour on iDBT was associated with being older, while greater days of use was related to immediate arm membership, identifying as non-Hispanic White or a sexual minority, and having fewer pretreatment depression/anxiety symptoms. Finally, inactivity at 4 weeks was predicted by pretreatment depression/anxiety symptoms. Conclusions: These exploratory analyses highlight several demographic and clinical variables of individuals who may require more support to achieve greater therapeutic benefits in self-guided iDBT interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".