Optimizing internet-delivered cognitive behaviour therapy for alcohol misuse: a study protocol for a randomized factorial trial examining the effects of a pre-treatment assessment interview and health educator guidance
Bibliographic record
Abstract
BACKGROUND: Alcohol misuse is a common, disabling, and costly issue worldwide, but the vast majority of people with alcohol misuse never access treatment for varying reasons. Internet-delivered cognitive behaviour therapy (ICBT) may be an attractive treatment alternative for individuals with alcohol misuse who are reluctant to seek help due to stigma, or who live in rural communities with little access to face-to-face treatment. With the growing development of ICBT treatment clinics, investigating ways to optimize its delivery within routine clinic settings becomes a crucial avenue of research. Some studies in the alcohol treatment literature suggest that assessment interviews conducted pre-treatment may improve short- and long-term drinking outcomes but no experimental evaluation of this has been conducted. Further, research on internet interventions for alcohol misuse suggests that guidance from a therapist or coach improves outcomes, but more research on the benefits of guidance in ICBT is still needed. METHODS: This study is a 2X2 factorial randomized controlled trial where all of the expected 300 participants receive access to the Alcohol Change Course, an eight-week ICBT program. A comprehensive pre-treatment assessment interview represents factor 1, and guidance from a health educator represents factor 2. All participants will be asked to respond to measures at screening, pre-treatment, mid-treatment, post-treatment and 3, 6 and 12 months after treatment completion. DISCUSSION: This study will provide valuable information on optimization of ICBT for alcohol misuse within routine clinic settings. TRIAL REGISTRATION: ClinicalTrials.gov, registered June 13th 2019, NCT03984786.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".