Formative Evaluation and Three-Month Follow-Up of an Online Personalized Assessment Feedback Intervention for Problem Drinkers
Bibliographic record
Abstract
BACKGROUND: In recent years, online services for problem drinkers have been developed. This paper describes ongoing efforts to improve one of these services, the Alcohol Help Center. OBJECTIVE: This report summarizes new modules added to the Check Your Drinking (CYD) screener, a component of the Alcohol Help Center, to make the CYD screener more useful to periodic heavy drinkers, as well as to regular alcohol consumers. Participants' initial reactions to the CYD screener and the changes in their drinking habits at a three-month follow-up are presented. METHODS: The CYD screener provides a free personalized Final Report that compares the user's drinking to that of others in the general population of the same age, gender, and country of origin. Current alcohol consumption and demographic characteristics are collected as part of the CYD screening process. After users were presented with a customized Final Report, they were hot-linked to a volunteer feedback survey. The voluntary feedback survey asked about impressions of the CYD Final Report. Respondents agreeing to participate were sent a follow-up survey after three months. RESULTS: We recruited 388 volunteers (69% female) who were registered users of another free-to-consumer online eHealth service. Of the 343 respondents agreeing to participate in the three-month follow-up, 138 accessed the survey, and 97 provided complete data (participation rate = 40%; completion rate = 70%). Compared to moderate drinkers, current problem drinkers judged the Final Report to be more useful (34% vs. 69%, chi2 (1) = 41.5, P < .001) and accurate (43% vs. 76%, chi2 (1) = 36.0, P < .001). Respondents who participated in the three-month follow-up displayed reductions in drinking compared to baseline (F(4,76) = 12.2, P = .001). CONCLUSIONS: Improvements can still be made to make the CYD screener more relevant to specific populations, particularly periodic heavy drinkers. There is a need to further tailor algorithms that can present questions only relevant to specific populations. There also appears to be a need to further customize the Final Report for respondents who identify themselves as infrequent heavy drinkers. These improvements will be made, and a randomized controlled trial is planned to conduct a rigorous evaluation of the CYD screener as an intervention to help 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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".