How Healthy and Unhealthy Food and Beverages Appear in Movies and Series for Children: A Comprehensive Content Analysis
Bibliographic record
Abstract
OBJECTIVE: Investigate which persuasive strategies are used in audiovisual media exposures of food and beverage items to children. DESIGN: Content analysis of movies and series based on questionnaire responses from children and their parents. SETTING: Six schools in rural as well as 6 schools in urban areas in Austria. PARTICIPANTS: A total of 648 children (aged 5-11 years) and 559 of their parents. MAIN OUTCOME MEASURES: Food and beverage categories (healthy = high nutritional value; unhealthy = high in fat, salt and/or sugars; mixed), composition-related, evaluative (nonverbal evaluation = facial expressions or sounds; verbal evaluation), and source-related strategies. ANALYSIS: A logistic mixed-effects regression model with random intercepts predicting nutritional classification. RESULTS: A total of 114 movies and 133 series (n = 12,320 food and beverage presentations) were coded. The study reveals that unhealthy food items are prominently featured in media aimed at children. Branded and centrally presented items had significantly higher odds of being classified as unhealthy (vs healthy; P < 0.001). Items that were interacted with or consumed were more likely to be unhealthy (vs healthy or mixed; P < 0.001). In addition, nonverbal positive evaluations increased the likelihood of items being unhealthy (vs healthy; P < 0.001), whereas negative nonverbal evaluations decreased the likelihood of items being unhealthy (vs healthy or mixed; P < 0.05). Conversely, items evaluated positively in a verbal-cognitive manner had lower odds of being unhealthy (vs healthy; P < 0.001). CONCLUSIONS AND IMPLICATIONS: This study calls for more research on the effects of verbal and nonverbal evaluations of food depictions, particularly for diverging evaluative cues. Findings also emphasize the call to further regulate the depiction of foods and beverages in movies and series while encouraging content creators to make more mindful choices in how these items are portrayed.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".