Can Brief Email Guidance Enhance the Effects of an Internet Intervention for People with Problematic Alcohol Use? A Randomized Controlled Trial
Bibliographic record
Abstract
Background Some research suggests that internet interventions aimed at people with problematic alcohol use are more effective when provided with guidance from a therapist or coach.Purpose/Objectives: This trial intended to compare the effects of a previously evaluated internet intervention for people with problematic alcohol use when delivered with or without brief email guidance. Methods: Using online advertising, 238 participants, 18 years or older, were recruited and randomized to receive access to the Internet intervention Alcohol Help Center with or without brief email guidance from a health educator. The guidance consisted of at least four structured, slightly individualized emails delivered during the first two weeks after randomization. Participants were followed up at 3 and 6 months. Results: Number of log-ins did not differ significantly between groups throughout the follow-up period. The follow-up rate at 6 months was 47.0%. Generalized estimating equations run on the primary (standard drinks in preceding week/heavy drinking days in preceding week) and secondary outcome variables (AUDIT, AUDIT-C, quality of life) revealed no significant differences between the interventions on any of the outcomes. Conclusions/Importance: The study does not provide support for any added benefits of providing brief guidance via email in an internet intervention for problem drinkers.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".