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 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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".