Feasibility of a Text Message–Based Alcohol Prevention Intervention for Parents of Rising Middle School Students: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Early-onset alcohol use (EOAU), or drinking before the age of 14 years, is a serious but highly preventable risk factor for later alcohol use. EOAU often begins at home, with sips of alcohol provided by parents. Few scalable interventions are available to engage parents in EOAU prevention. Objective: This study aimed to evaluate the feasibility of Better-Informed Parents Keeping Adolescents Safe From Alcohol (BIPAS Alcohol), a digital family-based intervention for parents of rising middle schoolers. Methods: In 2023-2024, we delivered BIPAS Alcohol to US parents (N=132) of 10- to 12-year-old children. The intervention consisted of a 3-month SMS text messaging curriculum and multimedia website. Guided by Bowen and colleagues' framework, we surveyed parents to evaluate our intervention on its feasibility, including acceptability, integration, demand, and adaptation. We interviewed a subset of parents (n=11) to probe survey findings. Results: Parents rated BIPAS Alcohol highly on acceptability, with almost all agreeing the intervention kept their attention (117/123, 95.1%), offered useful information (121/123, 98.4%), and helped reduce chances of underage drinking (119/123, 96.7%). Most parents indicated plans to integrate the intervention into family life by referring to content in the future (113/123, 91.9%) or sharing content with others (107/123, 87.0%). In interviews, parents expressed high demand for SMS text messages, due to their short, "digestible" format, while finding the website more cumbersome. Although we designed the SMS text message curriculum for adults, some parents reported adapting the intervention by sharing texts with their children. Conclusions: Our digital family-based intervention demonstrated feasibility and warrants additional evaluation in a larger-scale trial with a wider audience.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".