Improving alcohol health literacy and reducing alcohol consumption: recommendations for Germany
Bibliographic record
Abstract
BACKGROUND: Although the detrimental health effects of alcohol are well established, consumption levels are high in many high-income countries such as Germany. Improving alcohol health literacy presents an integrated approach to alcohol prevention and an important complement to alcohol policy. Our aim was to identify and prioritize measures to enhance alcohol health literacy and hence to reduce alcohol consumption, using Germany as an example. METHODS: A series of recommendations for improving alcohol health literacy were derived from a review of the literature and subsequently rated by five experts. Recommendations were rated according to their likely impact on enhancing (a) alcohol health literacy and (b) reducing alcohol consumption. Inter-rater agreement was assessed using a two-way intra-class correlation coefficient (ICC). RESULTS: Eleven recommendations were established for three areas of action: (1) education and information, (2) health care system, and (3) alcohol control policy. Education and information measures were rated high to increase alcohol health literacy but low to their impact on alcohol consumption, while this pattern was reversed for alcohol control policies. The ratings showed good agreement (ICC: 0.85-0.88). CONCLUSIONS: Improving alcohol health literacy and reducing alcohol consumption should be considered complementary and become part of a comprehensive alcohol strategy to curb the health, social, and economic burden of alcohol.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".