Assessing the impact of providing digital product information on the health risks of alcoholic beverages to the consumer at point of sale: A pilot study
Bibliographic record
Abstract
INTRODUCTION: There is an ongoing policy debate in the European Union regarding the best method of providing information to consumers on the health risks of alcohol use. One of the proposed channels is via the provision of QR codes. This study tested the usage rate of QR codes placed on point-of-sale signs in a supermarket in Barcelona, Catalonia over a 1-week period. METHODS: Nine banners with beverage-specific health warnings in large text were prominently displayed in the alcohol section of a supermarket. Each banner provided a QR code of relatively large image size that linked to a government website providing further information on alcohol-related harms. A comparison was made between the number of visits to the website and the number of customers in the supermarket (number of unique sales receipts) in a single week. RESULTS: Only 6 out of 7079 customers scanned the QR code during the week, corresponding to a usage rate of 0.085%, less than 1 per 1000. The usage rate was 2.6 per 1000 among those who purchased alcohol. DISCUSSION AND CONCLUSIONS: Despite the availability of prominently displayed QR codes, the overwhelming majority of customers did not make use of the QR codes to obtain further information on alcohol-related harms. This corroborates the results from other studies investigating customers' use of QR codes to obtain additional product information. Based on the current evidence, providing online access to information through QR codes will likely not reach a significant portion of consumers.
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 | 0.009 | 0.019 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".