A randomized controlled trial of an internet‐based intervention for alcohol abusers
Bibliographic record
Abstract
OBJECTIVE: Misuse of alcohol imposes a major public health cost, yet few problem drinkers are willing to access in-person services for alcohol abuse. The development of brief, easily accessible ways to help problem drinkers who are unwilling or unable to seek traditional treatment services could therefore have significant public health benefit. The objective of this project is to conduct a randomized controlled evaluation of the internet-based Check Your Drinking (CYD) screener ( http://www.CheckYourDrinking.net). METHOD: Participants (n = 185) recruited through a general telephone population survey were assigned randomly to receive access to the CYD, or to a no-intervention control group. RESULTS: Follow-up rates were excellent (92%). Problem drinkers provided access to the CYD displayed a six to seven drinks reduction in their weekly alcohol consumption (a 30% reduction in typical weekly drinking) at both the 3- and 6-month follow-ups compared to a one drink per week reduction among control group respondents. CONCLUSIONS: The CYD is one of a growing number of internet-based interventions with research evidence supporting its efficacy to reduce alcohol consumption. The internet could increase the range of help-seeking options available because it takes treatment to the problem drinker rather than making the problem drinker come to treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".