Food and beverage cues in UK and Irish children--television programming
Bibliographic record
Abstract
OBJECTIVES: Increased time in which children spend watching television is a well-described contributor to paediatric obesity. This study investigated the frequency and type of food and beverage placement in children-specific television broadcasts and compared data from UK (UK) and Irish television stations. DESIGN: Content analysis, totalling 82.5 h, reflecting 5 weekdays of children-specific television broadcasting on UK and Irish television channels was performed. To allow comparison between UK and Irish food and beverage cues, only broadcasts between 06.00 and 11.30 were analysed. Data were coded separately by two analysts and transferred to SPSS for analyses. Food and beverage cues were coded based on type of product, product placement, product use, motivation, outcome and characters involved. RESULTS: A total of 1155 food and beverage cues were recorded. Sweet snacks were the most frequent food cue (13.3%), followed by sweets/candy (11.4%). Tea/coffee was the most frequent beverage cue (13.5%), followed by sugar-sweetened beverages (13.0%). The outcome of the cue was positive in 32.6%, negative in 19.8%, and neutral in 47.5% of cases. The most common motivating factor associated with each cue was celebratory/social (25.2%), followed by hunger/thirst (25.0%). Comparison of UK and Irish placements showed both to portray high levels of unhealthy food cues. However, placements for sugar-sweetened beverages were relatively low on both channels. CONCLUSIONS: This study provides further evidence of the prominence of unhealthy foods in children's programming. These data may provide guidance for healthcare professionals, regulators and programme makers in planning for a healthier portrayal of food and beverage in children's television.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".