Can a Label Help me Drink in Moderation? A Review of the Evidence on Standard Drink Labelling
Bibliographic record
Abstract
INTRODUCTION: Understanding the concept of a standard drink (SD) is foundational knowledge to many public health policies aimed at reducing alcohol-related harms. These policies include adhering to low-risk drinking guidelines, screening brief intervention and referral activities, and counter alcohol-impaired driving initiatives. A lack of awareness of SDs might preclude the effectiveness of these interventions. A systematic review was conducted to review the evidence about how effective alcohol labels are in communicating SD information to the consumer. METHODS: A systematic review was conducted to identify peer-reviewed articles and grey literature from relevant indexes from January 1990 to January 2016. Additionally, policy makers and researchers in countries where standard drink labels (SDLs) have been implemented were consulted to help identify relevant literature. The search strategy was focused on the impact of SDLs relative to a range of outcomes, including awareness of SDs, pouring behaviors, and consumption patterns. RESULTS: Eleven records were eligible for inclusion. The evidence suggests that knowledge of the definition of an SD is low. However, SDLs can help individuals more accurately identify and pour an SD. SDLs need to be supported by educational initiatives to help the consumer understand the SD information provided on the beverage container. To date, there has been no comprehensive evaluation of the impact of SDLs. CONCLUSIONS: SDLs have the potential to increase awareness of SDs and facilitate the monitoring of personal alcohol consumption in the context of a comprehensive alcohol strategy. However, their impact on drinking behaviors requires further exploration, especially among high-risk populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".