The extent and nature of television food and non-alcoholic beverage advertising to children during chinese New Year in Beijing, China
Bibliographic record
Abstract
BACKGROUND: Exposure to food and non-alcoholic beverage advertisements (F&B ads) on television, which can affect children's nutrition knowledge, food consumption, diet quality, and purchasing preferences, is one aspect of the obesogenic environment. This aspect has been well-studied and assessed in many countries. In China, however, only few studies have been done in earlier years and all of them were focus on regular days. This study aimed to assess the extent and nature of F&B ads on television (TV) during the public holiday directed towards children aged 4-14 years in Beijing. METHOD: Top 3 channels viewed by children aged 4-14 years in Beijing were selected by TV viewership data, survey, and expert consultation. Each channel was recorded for 7 days (24 h) during the public holiday of the Chinese New Year in 2019. F&B ads were coded and analyzed following the adapted food promotion module of INFORMAS protocol. Three nutrient profile models were used to classify F&B ads as healthy or unhealthy F&B ads. RESULTS: Of the 10,082 ads in 504-hour recorded programs, 42.9% were F&B ads. The hourly average ads and F&B ads per channel were 19.8 (SD 15.32) and 8.6 (SD 9.84), while that was higher on the national children's channel (17.15, SD 12.25) than other channels (p < 0.05). Of F&B ads classified with the three nutrient profile models, more than 55% were unhealthy for children. The categories most frequently advertised were savory snacks, milk drinks, nonpermitted milk drinks, cakes/sweet biscuits, and beverages. Unhealthy F&B ads were more likely to use promotional characters, brand benefit claims, and health claims than permitted F&B ads (p < 0.05). CONCLUSIONS: Children in Beijing were exposed to a high proportion of unhealthy F&B ads during the Chinese New Year holiday. Our findings support the need to assess and regulate TV F&B ads marketing for children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".