Child-directed marketing on packaged breakfast cereals in South Africa
Bibliographic record
Abstract
OBJECTIVE: Childhood obesity is on the rise in South Africa (SA), and child-directed marketing (CDM) is one of the contributing factors to children's unhealthy food choices. This study assessed CDM on packaged breakfast cereals available in SA supermarkets and their nutritional quality. DESIGN: Photographic images were examined in a descriptive quantitative study. A codebook of definitions of CDM was developed for this purpose. REDCap, an online research database, was used for data capturing, and SPSS was used for data analyses including cross-tabulations and one-way ANOVA. SETTING: The current study was set in the Western Cape province of SA. SUBJECTS: Photographic images of all packaged breakfast cereals sold in major retailers in the Western Cape province of SA in 2019 were studied. RESULTS: CDM strategies were classified as direct (to the child) or indirect (through the parent). A total of 222 breakfast cereals were studied, of which 96·9 % had a nutritional or health claim, 95·0 % had illustrations, 75·2 % had product and consumption appeals, 10·8 % had characters, 10·8 % consisted of different appeals, 8·6 % alluded to fantasy and 7·7 % had role models. In breakfast cereals with direct CDM, the protein and fibre content was significantly lower than in breakfast cereals without direct CDM. This study found a significantly higher total carbohydrate and total sugar content in breakfast cereals with direct CDM than those without direct CDM. CONCLUSION: CDM was highly prevalent in breakfast cereals sold in SA. Regulations to curb the marketing of packaged foods high in nutrients of concern is recommended.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".