Optimizing internet-delivered cognitive behaviour therapy for alcohol misuse—a randomized factorial trial examining effects of a pre-treatment assessment interview and guidance
Bibliographic record
Abstract
BACKGROUND: Internet-delivered cognitive behavioral therapy (ICBT) for alcohol misuse has potential to radically improve access to evidence-based care, and there is a need to investigate ways to optimize its delivery in clinical settings. Guidance from a clinician has previously been shown to improve drinking outcomes in ICBT, and some studies suggest that pre-treatment assessments may contribute in initiating early change. The objective of this study was to investigate the added and combined effects of a pre-treatment assessment interview and guidance on the outcomes of ICBT for alcohol misuse delivered in an online therapy clinic. METHODS: A 2X2 factorial randomized controlled trial was conducted where participants received access to an 8-week ICBT program, and either a pre-treatment assessment interview (Factor 1), guidance (Factor 2), a combination of these, or neither of these. Participants were 270 individuals (66.8% female, mean age = 46.5) scoring 8 or more on the Alcohol Use Disorders Identification Test and consuming 14 standard drinks or more in the preceding week. Primary outcomes were number of drinks consumed and number of heavy drinking days during the preceding week, 3 months post-treatment. RESULTS: ≥ 0.82, p < 0.001), but neither of the factors significantly improved drinking outcomes. Guidance was associated with greater adherence (i.e. completed modules). CONCLUSIONS: Neither a pre-treatment assessment interview nor guidance from a clinician appears to improve drinking outcomes resulting from internet-delivered cognitive behaviour therapy for alcohol misuse when delivered in a routine online therapy clinic. TRIAL REGISTRATION: NCT03984786. Registered 13 June 2019, https://clinicaltrials.gov/ct2/show/NCT03984786.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".