Education‐based differences in alcohol health literacy in Germany
Bibliographic record
Abstract
INTRODUCTION: Alcohol health literacy is critical for informed consumer decision making but has yet received limited attention in public health research. We therefore seek to measure alcohol health literacy and its educational distribution in Germany. METHODS: In this cross-sectional study, we developed and applied a brief nine-item questionnaire on alcohol health literacy in an adult convenience sample (n = 391; February to April 2023). The association of educational attainment with 'insufficient' alcohol health literacy was tested in adjusted logistic regression models. RESULTS: Insufficient alcohol health literacy was recorded in 47.8% of men and 41.1% of women in our sample. While most respondents correctly identified common misconceptions and wrong beliefs about alcohol and were able to specify low-risk drinking limits for women and women during pregnancy, only few correctly identified all alcohol-related health conditions, especially respiratory and infectious diseases. Respondents with low education were 1.35 (risk ratio [RR], 95% confidence interval 1.09-1.50, p = 0.014) times more likely to have been classified as having insufficient alcohol health literacy than high-educated respondents. There was no statistically significant difference between respondents with medium versus high education (RR = 1.22, 95% confidence interval 0.99-1.43, p = 0.060). DISCUSSION AND CONCLUSIONS: Educational gaps in alcohol health literacy question a policy rationale that is fundamentally based on the premise of informed consumer choice. Strategies to raise alcohol health literacy must ensure that they reach all population groups, for instance, by providing health warning labels on alcohol containers.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".