Screen Media, Obesity, and Nutrition
Bibliographic record
Abstract
Abstract This chapter provides an update on and the synthesis of the most current evidence on screen use, nutritional intake, and obesity in youth. The strongest evidence of a mechanism linking screen media use to obesity is through repeated exposures to unhealthy food marketing and excess eating while viewing screens. Evidence of the neurocognitive effects on dietary consumption via the effects of executive function, satiety, and memory is emerging. Interventions to reduce children’s screen time or to incorporate screen media into improving nutrition and obesity outcomes have had mixed results. The evidence base is also limited by imprecise measurements of screen use and a focus on the total duration of screen use without capturing the type, content, quality, or interactivity. Recommendations are offered to researchers, clinicians, providers, policymakers, advocates, and industry leaders to develop and validate accurate methods to capture the content, context, functions, timing, and quantity of children’s screen time to better understand linkages to children’s dietary intake and dietary habits; to counsel families on health boundaries for screen media use; to develop effective strategies to reduce sedentary screen use; and to expand parental control features and regulate industry to limit children’s exposure to food marketing and food placement on screen media.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".