Optimizing a Text Message Intervention to Reduce Heavy Drinking in Young Adults: Focus Group Findings
Bibliographic record
Abstract
BACKGROUND: Recent trial results show that an interactive short message service (SMS) text message intervention, Texting to Reduce Alcohol Consumption (TRAC), is effective in reducing heavy drinking in non-treatment-seeking young adults, but may not be optimized. OBJECTIVE: To assess the usability of the TRAC intervention among young adults in an effort to optimize future intervention design. METHODS: We conducted five focus groups with 18 young adults, aged 18-25 years, who had a history of heavy drinking and had been randomized to 12 weeks of the TRAC intervention as part of a clinical trial. A trained moderator followed a semistructured interview guide. Focus groups were audiotaped, transcribed, and analyzed to identify themes. RESULTS: We identified four themes regarding user experiences with the TRAC intervention: (1) ease of use, (2) comfort and confidentiality, (3) increased awareness of drinking behavior, and (4) accountability for drinking behavior. Participants' comments supported the existing features of the TRAC intervention, as well as the addition of other features to increase personalization and continuing engagement with the intervention. CONCLUSIONS: Young adults perceived the TRAC intervention as a useful way to help them reduce heavy drinking on weekends. Components that promote ease of use, ensure confidentiality, increase awareness of alcohol consumption, and increase accountability were seen as important.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".