The relationship between parent's self-reported exposure to food marketing and child and parental purchasing and consumption outcomes in five countries: findings from the International Food Policy Study
Bibliographic record
Abstract
Food and beverage marketing influences children's food preferences and dietary intake. Children's diets are also heavily influenced by their family environment. The aim of this study was to assess the relationship between parent's self-reported exposure to unhealthy food marketing and a range of outcomes related to children's desire for and intake of unhealthy foods and beverages. The study also sought to examine whether these outcomes varied across different countries. The analysed data are from the International Food Policy Study and were collected in 2018 using an online survey. The sample included 5764 parents of children under 18, living in Australia, Canada, Mexico, the United Kingdom, or the United States. Binary logistic regressions assessed the link between the number of parental exposure locations and children's requests for and parental purchases of unhealthy foods. Generalized ordinal regression gauged the relationship between the number of exposure locations and children's consumption of such items. Interaction terms tested if these associations varied by country. Parental exposure to unhealthy food marketing was positively associated with parents reporting child purchase requests and purchase outcomes; and differed by country. Increased parental exposure to unhealthy food marketing was associated with slightly lower odds of children's weekly consumption of unhealthy foods, and this association varied by country. In conclusion, parental report of a greater range of food marketing exposure was associated with a range of outcomes that would increase children's exposure to unhealthy food products or their marketing. Governments should consider developing more comprehensive restrictions on the marketing of unhealthy foods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".