A Baby Formula Designed for Chinese Babies: Content Analysis of Milk Formula Advertisements on Chinese Parenting Apps
Bibliographic record
Abstract
BACKGROUND: China is the largest market for infant formula. With the increasing use of smartphones, apps have become the latest tool used to promote milk formula. Formula manufacturers and distributors both have seized the popularity of apps as an avenue for marketing. OBJECTIVE: This study aimed to identify and analyze milk formula ads featured on Chinese pregnancy and parenting apps, to build the first complete picture of app-based milk formula marketing techniques being used by milk formula brand variants on these apps, and to more fully understand the ad content that potentially undermines public health messaging about infant and young child feeding. METHODS: We searched for free-to-download Chinese parenting apps in the 360 App Store, the biggest Android app store in China. The final sample consisted of 353 unique formula ads from the 79 apps that met the inclusion criteria. We developed a content analysis coding tool for categorizing the marketing techniques used in ads, which included a total of 22 coding options developed across 4 categories: emotional imagery, marketing elements, claims, and advertising disclosure. RESULTS: The 353 milk formula ads were distributed across 31 companies, 44 brands, and 79 brand variants. Overall, 15 of 31 corporations were international with the remaining 16 being Chinese owned. An image of a natural pasture was the most commonly used emotional image among the brand variants (16/79). All variants included branding elements, and 75 variants linked directly to e-shops. Special price promotions were promoted by nearly half (n=39) of all variants. A total of 5 variants included a celebrity endorsement in their advertising. A total of 25 of the 79 variants made a product quality claim. Only 14 variants made a direct advertisement disclosure. CONCLUSIONS: The purpose of marketing messages is to widen the use of formula and normalize formula as an appropriate food for all infants and young children, rather than as a specialized food for those unable to breastfeed. Policy makers should take steps to establish an appropriate regulatory framework and provide detailed monitoring and enforcement to ensure that milk formula marketing practices do not undermine breastfeeding norms and behaviors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".