Computerised cognitive behavioural therapy for alcohol use disorder: a pilot randomised control trial
Bibliographic record
Abstract
BACKGROUND: Cognitive behavioural therapy (CBT) has been used in the treatment of alcohol use disorder (AUD), generally in individual or group therapy, but not via computer. Aim This study examined the effectiveness of an interactive, personalised, computer-based CBT therapy in a randomised control trial. METHODS: We studied a group of 55 patients with AUD, randomised to either 5-hour-long computerised CBT sessions or a placebo cognitive-stimulating session, together with a 4-week inpatient rehabilitation treatment, and followed them for 3 months. RESULTS: There was a high degree of patient adherence to the protocol. Both groups did well, with a significant fall in alcohol outcome measures including number of drinks per drinking day, and number of drinking days, and an increase in abstinence rates in both groups to an equivalent level. The CBT group attended alcoholics anonymous groups more frequently, and had significant alterations in their alcohol self-efficacy outcomes, which correlated with their drinking outcomes. We concluded that computerised CBT is a potentially useful clinical tool that warrants further investigation in different treatment settings for AUD.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".