Applying and comparing various nutrient profiling models against the packaged food supply in South Africa
Bibliographic record
Abstract
OBJECTIVE: This study aimed to apply the newly developed Chile Adjusted Model (CAM) nutrient profiling model (NPM) to the food supply in South Africa (SA) and compare its performance against existing NPM as an indication of suitability for use to underpin food policies targeted at discouraging consumption of products high in nutrients associated with poor health. DESIGN: Cross-sectional analysis of the SA-packaged food supply comparing the CAM to three other NPM: SA Health and Nutrition Claims (SA HNC), Chilean Warning Octagon (CWO) 2019, and Pan-American Health Organisation (PAHO) NPM. SETTING: The SA-packaged food supply based on products stocked by supermarkets in Cape Town, SA. PARTICIPANTS: 6474) available in 2018 were analysed. RESULTS: Forty-nine per cent of products contained excessive amounts of nutrients of concern (considered non-compliant) according to the criteria of all four models. Only 10·9 % of products were not excessive in any nutrients of concern (considered compliant) according to all NPM evaluated. The CAM had an overall non-compliance level of 73·2 % and was comparable to the CWO 2019 for foods (71·2 % and 71·1 %, respectively). The CAM was the strictest NPM for beverages (80·4 %) due to the criteria of non-sugar sweeteners and free sugars. The SA HNC was the most lenient with non-compliance at 52·9 %. This was largely due to the inclusion of nutrients to encourage, which is a criterion for this NPM. CONCLUSION: For the purpose of discouraging products high in nutrients associated with poor health in SA, the CAM is a suitable NPM.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".