Package size and manufacturer-recommended serving size of sweet beverages: a cross-sectional study across four high-income countries
Bibliographic record
Abstract
OBJECTIVE: To assess the mean package size and manufacturer-recommended serving size of sweet beverages available in four high-income countries: Australia, Canada, the Netherlands and New Zealand. DESIGN: Cross-sectional surveys. SETTING: The two largest supermarket chains of each country in 2012/2013. SUBJECTS: Individual pack size (IPS) drinks (n 891) and bulk pack size (BPS) drinks (n 1904). RESULTS: For all IPS drinks, the mean package size was larger than the mean serving size (mean (sd)=412 (157) ml and 359 (159) ml, respectively). The mean (sd) package size of IPS drinks was significantly different for all countries (range: Australia=370 (149) ml to New Zealand=484 (191) ml; P<0·01). The mean (sd) package size of Dutch BPS drinks (1313 (323) ml) was significantly smaller compared with the other countries (New Zealand=1481 (595) ml, Australia=1542 (595) ml, Canada=1550 (434) ml; P<0·01). The mean (sd) serving size of BPS drinks was significantly different across all countries (range: Netherlands=216 (30) ml to Canada=248 (31) ml; P<0·00). New Zealand had the largest package and serving sizes of the countries assessed. In all countries, a large number of different serving sizes were used to provide information on the amount appropriate to consume in one sitting. CONCLUSIONS: At this point there is substantial inconsistency in package sizes and manufacturer-recommended serving sizes of sweet beverages within and between four high-income countries, especially for IPS drinks. As consumers do factor serving size into their judgements of healthiness of a product, serving size regulations, preferably set by governments and global health organisations, would provide consistency and assist individuals in making healthier food choices.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".