Smartphone-based secondary prevention intervention for university students with unhealthy alcohol use identified by screening: study protocol of a parallel group randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Unhealthy alcohol use is a leading cause of morbidity and mortality among young people, including university students. Delivering secondary prevention interventions against unhealthy alcohol use is challenging. Information technology has the potential to reach large parts of the general population. The present study is proposed to test a proactive secondary prevention smartphone-based intervention against unhealthy alcohol use. METHODS: This is a parallel-group, randomized controlled trial (1:1 allocation ratio) among 1696 university students with unhealthy alcohol use, identified by screening and followed up at 3, 6, and 12 months. Participants will be randomized to receive access to a smartphone-based intervention or to a no intervention control condition. The primary outcome will be self-reported volume of alcohol drunk over the past 30 days, reported as the mean number of standard drinks per week over the past 30 days, measured at 6 months. Secondary outcomes will be number of heavy drinking days over the past 30 days, at 6 months. Additional outcomes will be maximum number of drinks on any day over the past 30 days, alcohol-related consequences (measured using the Short Inventory of Problems (SIP-2R), and academic performance. DISCUSSION: The aim of this trial is to close the evidence gap on the efficacy of smartphone-based secondary prevention interventions. If proven effective, smartphone-based interventions have the potential to reach a large portion of the population, completing what is available on the Internet. TRIAL REGISTRATION: ISRCTN, 10007691. Registered on 2 December 2019. Recruitment will start in April 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".