Think-Aloud Testing of a Novel Safer Drinking App for College Students During COVID-19: Usability Study
Bibliographic record
Abstract
BACKGROUND: Hazardous alcohol consumption, and binge drinking in particular, continues to be common among college students, posing the greatest risk for their health and safety. Despite widespread exposure to evidence-based preventive interventions among US undergraduates, only modest and temporary effects on risky drinking occur. Formative studies have demonstrated that students want a more engaging intervention tool for risky drinking that can be used just in time. OBJECTIVE: The purpose of this study is to test the appeal, relevance, and perceived utility of a draft mobile app for safer student drinking at a public university in Virginia. METHODS: Undergraduate student participants tested the draft mobile app via a web-based prototype that tailors to individual feedback with hot spots that responded to their taps to mimic app functionality. They narrated their impressions, navigation, and comments in a standardized think-aloud procedure. After each round of think-aloud interviews, researchers debriefed the investigators and developers to discuss findings and brainstorm app modifications. RESULTS: Minor changes to the functionality and aesthetics would improve usability of the app (eg, option for light mode in app settings). Student testers recommended tailoring the app to the needs of college students and to aspects of the local university's drinking culture. CONCLUSIONS: Findings from this study will be synthesized with information gained from other formative work to determine the final app features. We will test the app in a pilot randomized trial to assess app use and the impact of the app on college student drinking behavior over several months.
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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 | 0.006 | 0.024 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".