Internet-Delivered Dialectical Behavioral Therapy Skills Training for Suicidal and Heavy Episodic Drinkers: Protocol and Preliminary Results of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The need to develop effective and accessible interventions for suicidal individuals engaging in heavy episodic drinking (HED) cannot be understated. While the link between alcohol use and suicidality is a complex one that remains to be elucidated, emotion dysregulation may play a key role in alcohol-related suicide risk in these individuals. OBJECTIVE: In the current study, an 8-week Internet-delivered dialectical behavior therapy (DBT) skills training intervention was developed and preliminarily evaluated for suicidal individuals who engage in HED to regulate emotions. The aim of the study is to evaluate the feasibility and effectiveness of the therapist-assisted and Internet-delivered intervention, and to inform the design of a subsequent full-scale study. METHODS: The study was a pilot randomized controlled trial comparing participants receiving immediate-treatment (n=30) to waitlist controls (n=29) over a period of 16 weeks. Intervention effects will be assessed longitudinally using hierarchical linear modeling and generalized estimating equations, along with analyses of effect sizes and clinically significant change. The primary outcomes are suicidal ideation, alcohol problems, and emotion dysregulation. Secondary outcomes include alcohol-related consequences, reasons for living, skills use, and depression. RESULTS: The trial is ongoing. A total of 60 individuals returned their informed consent and were randomized, of whom 59 individuals were intended to treat. A total of 50 participants in the study were retained through the 16-week enrollment. CONCLUSIONS: There is a dearth of evidence-based treatment for individuals presenting with high risk and complex behaviors. Furthermore, computerized interventions may provide a beneficial alternative to traditional therapies. The particular clinical features and treatment needs of suicidal individuals who also engage in HED constitute key domains for further investigation that are needed to consolidate the design of appropriate interventions for this high-risk population. TRIAL REGISTRATION: Clinicaltrials.gov NCT02932241; https://clinicaltrials.gov/ct2/show/NCT02932241 (Archived by WebCite at http://www.webcitation.org/6uJHdQsC2).
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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 | 0.043 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".