Trans-fat labelling and potential presence of industrially produced trans-fat in the New Zealand packaged food supply: 2015–2019 & 2022
Bibliographic record
Abstract
BACKGROUND AND AIM: The World Health Organization (WHO) recommends that countries reduce industrially produced TFA (iTFA) in the food supply. However, New Zealand (NZ) has no mandatory regulation to control amounts of iTFA in foods. The objectives of this study were to assess within the NZ packaged food supply in recent years (2015-19 and 2022): i) the availability of products displaying information on TFA content on nutrition information panels (NIPs), ii) the content of TFA declared, and iii) the presence/potential presence of iTFA (n = 85,892 products). METHODS AND RESULTS: A database of packaged foods from major NZ supermarkets was used. TFA contents declared on NIPs were benchmarked against limits recommended by the WHO and the Canadian Trans Fat Task Force. Proportions of products listing specific ingredients (containing iTFA) or non-specific ingredients (potentially containing iTFA) were examined. Trends in proportions were assessed (Mantel-Haenszel tests). Among all products and years examined (n = 81,591), 84.0 % did not display information on TFA content. Across all products declaring TFA contents and years 15.4 % and 6.4 %, respectively, were above the WHO and Canadian TFA limits. Across all products and years, 0.8 % and 13.6 % listed ingredients that contained or potentially contained iTFA, respectively. Across 2015-2019, there was a trend of decrease in the proportions of products listing specific (0.9 %-0.7 %; P = 0.018) and non-specific ingredients (15.1 %-12.8 %; P < 0.001). CONCLUSION: Information on the TFA content and ingredients containing iTFA in NZ packaged foods is lacking and ambiguous and government-led interventions to control and reduce TFA in the food supply are warranted.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".