Differential exposure to, and potential impact of, unhealthy advertising to children by socio‐economic and ethnic groups: A systematic review of the evidence
Bibliographic record
Abstract
Children's exposure to advertising of unhealthy food and nonalcoholic beverages that are high in saturated fats, salt and/or sugar is extensive and increases children's preferences for, and intake of, targeted products. This systematic review examines the differential potential exposure and impact of unhealthy food advertising to children according to socio-economic position (SEP) and/or ethnicity. Nine databases (health, business, marketing) and grey literature were searched in November 2019 using terms relating to 'food or drink', 'advertising' and 'socioeconomic position or ethnicity'. Studies published since 2007 were included. Article screening and data extraction were conducted by two independent reviewers. Quality of studies was assessed using the Newcastle-Ottawa quality scale. Of the 25 articles included, 14 focused on exposure to unhealthy food advertising via television, nine via outdoor mediums and two via multiple mediums. Most studies (n = 19) revealed a higher potential exposure or a greater potential impact of unhealthy food advertising among ethnic minority or lower SEP children. Few studies reported no difference (n = 3) or mixed findings (n = 3). Children from minority and socio-economically disadvantaged backgrounds are disproportionately exposed to unhealthy food advertising. Regulations to restrict unhealthy food advertising to children should be implemented to improve children's diets and reduce inequities in dietary intake.
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 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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".