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Estimated Exposure to Televised Alcohol Advertisements Among Children and Adolescents

2025· article· en· W4412488910 on OpenAlexafffund
Yuxiang Tang, Nan Lei, Denghui Hu, Yang Liu, Tilakavati Karupaiah, Bridget Kelly, Sally Mackay, Boyd Swinburn, Juan Zhang

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMemorial University of Newfoundland
FundersPeking Union Medical CollegeChinese Academy of Medical SciencesInternational Development Research Centre
KeywordsAdvertisingBeijingChinaEnvironmental healthMedicineFood marketingPsychologyGeographyBusiness

Abstract

fetched live from OpenAlex

Importance: Alcohol advertising on television in China has the potential to target children and adolescents with harmful content. Understanding the extent of this advertising is critical for informing and improving current regulatory approaches. Objective: To measure the exposure of alcohol advertisements on television channels popular among children and adolescents in Beijing, China. Design, Setting, and Participants: This cross-sectional study of television advertisements used the 4 most popular television channels for viewers aged 3 to 18 years (2 children's channels and 2 general channels) in Beijing and accessed advertisements recorded from October 19, 2020, to January 17, 2021. Television advertisements were recorded during 4 randomly selected weekdays and 4 randomly selected weekend days (from 6:00 am to 11:59 pm). Data were analyzed from October 1, 2023, to December 31, 2024. Exposures: Television alcohol advertisements, with food and nonalcoholic beverages (F&B) advertisements classified as not permitted in marketing to children included as comparison. Main Outcomes and Measures: Primary outcomes included frequency and distribution of alcohol advertisements, rate per channel-hour, and potential exposure during peak viewing times (PVT). Secondary outcomes included comparison with F&B advertisements classified as not permitted based on the World Health Organization Western Pacific Region Office Nutrient Profile Model integrated with the International Network for Food and Obesity/Non-communicable Diseases Research, Monitoring and Action Support (INFORMAS) food classification system and analysis of 6 marketing strategies. Results: Among 13 864 total advertisements included in the analysis, 5368 were food advertisements. Among the food advertisements, 321 (6.0%; 95% CI, 5.4%-6.7%) were alcohol advertisements and 2001 (37.3%; 95% CI, 36.0%-38.6%) were F&B advertisements classified as not permitted. On general channels, a mean (SD) of 1.1 (1.7) alcohol advertisements per channel-hour were identified, with significantly higher rates during PVT compared with non-PVT (2.0 [2.4] vs 0.7 [0.9] per channel-hour; P < .001). The highest rate occurred between 9:00 and 9:59 pm, with a mean (SD) of 3.7 (2.8) advertisements per channel-hour and an estimated mean (SD) of 14 303 014 (11 659 096) impressions among children and adolescents. All 321 alcohol advertisements (100%; 95% CI, 98.9%-100%) and 1997 F&B advertisements classified as not permitted (99.8%; 95% CI, 99.5%-99.9%) used at least 1 marketing strategy, predominantly brand benefit claims, which were used in 307 alcohol advertisements (95.6%; 95% CI, 92.8%-97.4%) and 1915 F&B advertisements classified as not permitted (95.7%; 95% CI, 94.7%-96.5%). Conclusions and Relevance: In this cross-sectional study of television advertising, alcohol advertisements on general channels exceeded regulatory limits, especially during PVT. These findings suggest that current regulations allow exposure of children and adolescents to alcohol marketing and should be strengthened.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.299
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes2
Has abstractyes

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