Designing and implementing an experimental survey on knowledge and perceptions about alcohol warning labels
Bibliographic record
Abstract
OBJECTIVES: This paper describes the design and implementation of an online survey experiment to investigate the effects of alcohol warning labels on alcohol-related knowledge, risk perceptions and intentions. METHOD: The survey collected self-reported data from 14 European countries through two waves of data collection with different recruitment strategies: dissemination via social media and public health agencies was followed by paid-for Facebook ads. The latter strategy was adopted to achieve broader population representation. Post-stratification weighting was used to match the sample to population demographics. RESULTS: The survey received over 34,000 visits and resulted in a sample size of 19,601 participants with complete data on key sociodemographic characteristics. The responses in the first wave were over-representing females and higher educated people, thus the dissemination was complemented by the paid-for Facebook ads targeting more diverse populations but had higher attrition rate. CONCLUSION: Experiments can be integrated into general population surveys. Pan-European results can be achieved with limited resources and a combination of sampling methods to compensate for different biases, and statistical adjustments.
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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 | 0.197 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".