Nutritional quality of new food products released into the Australian retail food market in 2015 – is the food industry part of the solution?
Bibliographic record
Abstract
BACKGROUND: Food manufacturers have made public statements and voluntary commitments, such as the Healthier Australia Commitment (HAC), to improve the nutritional quality of foods. However, limited information about the nutritional quality or healthfulness of new products makes it difficult to determine if manufacturers are doing this. The purpose of this study was to assess the healthfulness of new food products released into the Australian retail market in 2015, and whether those companies who were HAC members released healthier food options compared to non-HAC members. METHODS: This cross-sectional study assessed the healthfulness of all new retail food products launched in Australia in 2015 as indexed in Mintel's Global New Products Database. Healthfulness was assessed using three classification schemes: Healthy Choices Framework Victoria, Australian Dietary Guidelines and NOVA Food Classification System. Descriptive statistics and chi-squared tests described and compared the number and proportions of new foods falling within each of the food classification schemes' categories for companies that were and were not HAC members. RESULTS: In 2015, 4143 new food products were launched into the Australian market. The majority of new products were classified in each schemes' least healthy category (i.e. red, discretionary and ultra-processed). Fruits and vegetables represented just 3% of new products. HAC members launched a significantly greater proportion of foods classified as red (59% vs 51% for members and non-members, respectively) discretionary (79% vs 61%), and ultra-processed (94% vs 81%), and significantly fewer were classified as green (8% vs 15%), core foods (18% vs 36%) and minimally processed (0% vs 6%) (all p < 0.001). CONCLUSIONS: This study found that the majority of new products released into the Australian retail food market in 2015 were classified in each of three schemes' least healthy categories. A greater proportion of new products launched by companies that publicly committed to improve the nutritional quality of their products were unhealthy, and a lower proportion were healthy, compared with new products launched by companies that did not so commit. Greater monitoring of industry progress in improving the healthfulness of the food supply may be warranted, with public accountability if the necessary changes are not seen.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".