Benchmarking unhealthy food marketing to children and adolescents in Canada: a scoping review
Bibliographic record
Abstract
Introduction: Unhealthy food and beverage marketing in various media and settings contributes to children’s poor dietary intake. In 2019, the Canadian federal government recommended the introduction of new restrictions on food marketing to children. This scoping review aimed to provide an up-to-date assessment of the frequency of food marketing to children and youth in Canada as well as children’s exposure to this marketing in various media and settings in order to determine where gaps exist in the research. Methods: For this scoping review, detailed search strategies were used to identify relevant peer-reviewed and grey literature published between October 2016 and November 2021. Two reviewers screened all results. Results: A total of 32 relevant and unique articles were identified; 28 were peer reviewed and 4 were from the grey literature. The majority of the studies (n = 26) examined the frequency of food marketing while 6 examined actual exposure to food marketing. Most research focussed on children from Ontario and Quebec and television and digital media. There was little research exploring food marketing to children by age, geographical location, sex/gender, race/ethnicity and/or socioeconomic status. Conclusion: Our synthesis suggests that unhealthy food marketing to children and adolescents is extensive and that current self-regulatory policies are insufficient at reducing the presence of such marketing. Research assessing the frequency of food marketing and preschooler, child and adolescent exposure to this marketing is needed across a variety of media and settings to inform future government policies.
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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 | 0.025 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.032 | 0.051 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".