A systematic review of internet-delivered cognitive behavior therapy for alcohol misuse: study characteristics, program content and outcomes
Bibliographic record
Abstract
Alcohol misuse is a common, disabling and costly issue worldwide. Internet-delivered cognitive behavior therapy (ICBT) has the potential to reduce the harms of alcohol misuse, particularly for individuals who are unable or unwilling to access face-to-face therapy. A systematic review was conducted using Medline, CINAHL, EMBASE and PsycINFO databases for all relevant articles published from 1980 to January 2019. Randomized controlled trials (RCTs) were included if (i) an ICBT intervention targeting alcohol misuse was delivered; (ii) participants were aged 18 years or older; and (iii) primary outcomes were quantity of drinking. A qualitative analysis was conducted on the content of the ICBT programs. Fourteen studies met inclusion criteria. Most studies included participants from the general population, while studies conducted within clinic settings with diagnosed individuals were rare. The programs were similar in terms of included modules. Small effects were seen in studies on self-guided ICBT, while therapist-guided ICBT rendered small to large effects. The current review indicates that ICBT has a significant effect in reducing alcohol consumption. Larger studies evaluating ICBT compared to active control groups especially within clinic settings are warranted.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".