Comparison of self-reported measures of alcohol-related dependence among young Swiss men: a study protocol for a cross-sectional controlled sample
Bibliographic record
Abstract
INTRODUCTION: Short screenings of alcohol-related dependence are needed for population-based assessments. A clinical interview constitutes a reliable diagnosis often seen as gold standard, but it is costly and time consuming and as such, not suitable for population-based assessments. Therefore, self-reported questionnaires are needed (eg, alcohol use disorder (AUD) as in the Diagnostic and Statistic Manual of Mental Disorders (DSM) 5), but their reliability is questionable. Recent studies called for more evidence-based measurements for population-based screening (eg, heavy alcohol use over time (HAU)). This study aims to test the reliability of different self-reported measures of alcohol use. METHODS AND ANALYSIS: Based on stratified random selection, 280 participants will be recruited from the French-speaking subgroup of the Swiss National Science Foundation-supported Cohort Study on Substance Use and Risk Factors (C-SURF). This cohort is a population-based sample of young Swiss men in their mid-20s (n=2668). The sample size calculation is based on a proportion non-inferiority test (alpha=5%, power=80%, margin of equivalence=10%, difference in sensitivity between self-reported AUD and HAU=5%, correlation between AUD and HAU=0.35, and drop-outs=15%). Assessment will include a clinical interview as the gold standard of alcohol-related dependence, self-reported alcohol measures (HAU, AUD and drinking patterns), biomarkers as gold standards of chronic excessive drinking, and health outcomes. To assess the validity of the self-reported alcohol measures, sensitivity analyses will be run. The associations between alcohol-related measures and health outcomes will be tested. A non-response analysis will be run using the previous waves of the C-SURF study using logistic regressions. ETHICS AND DISSEMINATION: The study protocol has been approved by the Human Research Ethics Committee of the Canton of Vaud, Switzerland (no. 2017-00776). The results will be submitted for publication in peer-reviewed journals and presented at national and international conferences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".