The effectiveness of alcohol warning labels in the prevention of Fetal Alcohol Spectrum Disorder: A brief review
Bibliographic record
Abstract
Thomas, G., Gonneau, G., Poole, N., & Cook, J. (2014). The effectiveness of alcohol warning labels in the prevention of Fetal Alcohol Spectrum Disorder: A brief review. The International Journal Of Alcohol And Drug Research, 3(1), 91-103 doi:10.7895/ijadr.vX3i1.126Aims and Method: Alcohol warning labels are one way of influencing alcohol consumption in pregnancy and thereby preventing Fetal Alcohol Spectrum Disorder (FASD). This scoping review describes the literature on the influence of alcohol warning labels (AWLs) for changing attitudes and behavior related to alcohol use in pregnancy; draws on the larger literature related to effectiveness of AWLs which may have relevance to FASD prevention; and situates AWLs within a continuum of strategies to prevent FASD.Findings: Our review of the published literature suggests that while AWLs are popular with the public, their effectiveness for changing drinking behavior is limited. Available research suggests that for maximum effect, AWLs should speak clearly about the consequences of alcohol consumption and should also be coordinated and integrated with other, broader social messaging campaigns. Use of AWLs related to alcohol and pregnancy must be carefully considered; their messaging has the most influence on low-risk drinkers, and to date they have not been shown to change the drinking behavior of those who drink heavily or binge during pregnancy. However, AWLs have been shown to stimulate conversations about alcohol consumption and may play a role in shifting social norms to reduce risks.Conclusions: Multiple measures are required to increase societal awareness of the risks of drinking in pregnancy, influence alcohol consumption by pregnant women, and improve the quality of support for women with alcohol and related health and social problems.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".