The impact of the social media industry as a commercial determinant of health on the digital food environment for children and adolescents: a scoping review
Bibliographic record
Abstract
INTRODUCTION: There is emerging evidence that the social media industry contributes to adverse health outcomes by shaping the digital food environment for children and adolescents (aged 0-18). The aim of this scoping review was to determine the extent of research on how the social media industry, including the broader online landscape, influences the digital food environment and affects child and adolescent health. METHODS: A scoping review was conducted in the electronic databases of PubMed, Scopus and PsycINFO, along with forward and reverse citation searching for peer-reviewed articles published in English between 2000 and May 2023. A qualitative descriptive synthesis of the included articles was performed to identify trends, themes and research gaps in the current literature. RESULTS: The review identified 36 articles for inclusion. Most research was conducted in high-income countries and publications have increased since 2021. The review found most children and adolescents are exposed to food advertisements on social media and most advertised food is ultra-processed. Heightened by a lack of social media advertising awareness, digital food marketing influences children and youth's consumption and food behaviour. Voluntary children's food marketing regulations are ineffective for the online environment. Countering unhealthy food marketing will require media literacy and government regulation. CONCLUSION: The social media industry may act as a commercial determinant of health to shape the digital food environment as an extension of the obesogenic environment. Further research should explore approaches to monitor unhealthy food marketing practices and understand social media's role in the digital food environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".