Treatment dismantling pilot study to identify the active ingredients in personalized feedback interventions for hazardous alcohol use: randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: There is a considerable body of evidence supporting the effectiveness of personalized feedback interventions for hazardous alcohol use-whether delivered face-to-face, by postal mail, or over the Internet (probably now the primary mode of delivery). The Check Your Drinking Screener (CYD; see www.CheckYourDrinking.net) is one such intervention. OBJECTIVES: The current treatment dismantling study assessed which components of personalized feedback interventions were effective in motivating change in drinking. Specifically, the major objective of this project was to conduct a randomized controlled trial (RCT) comparing the impact of the normative feedback and other personalized feedback components of the CYD intervention in the general population. METHODS: Participants were recruited to take part in an RCT and received either the complete CYD final report, just the normative feedback sections of the CYD, just the personalized feedback components of the CYD, or were assigned to a no-intervention control group. Participants were followed-up at 3 months to assess changes in alcohol consumption. RESULTS: A total of 741 hazardous drinking participants were recruited for the trial, of which 73 percent provided follow-up data. Analyses using an intent-to-treat approach found some evidence for the impact of the personalized feedback components of the CYD in reducing alcohol consumption on the variables, number of drinks in a week and AUDIT-C (p = .028 and .047 respectively; no impact on highest number of drinks on one occasion; p = .594). However, there was no significant evidence of the impact of the normative feedback components (all p > .3). CONCLUSIONS: Personalized feedback elements alone could provide an active intervention for hazardous drinkers, particularly in situations where normative feedback information was not available. TRIALS REGISTRATION: ClinicalTrials.gov NCT01608763.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".