The Power of Packaging: A Scoping Review and Assessment of Child-Targeted Food Packaging
Bibliographic record
Abstract
Child-targeted food marketing is a significant public health concern, prompting calls for its regulation. Product packaging is a powerful form of food marketing aimed at children, yet no published studies examine the range of literature on the topic or the "power" of its marketing techniques. This study attempts such a task. Providing a systematic scoping review of the literature on child-targeted food packaging, we assesses the nutritional profile of these foods, the types of foods examined, and the creative strategies used to attract children. Fifty-seven full text articles were reviewed. Results identify high level trends in methodological approaches (content analysis, 38%), outcomes measured (exposure, 44%) and with respect to age. Studies examining the nutritional profile of child-targeted packaged foods use various models, classifying from anywhere from 41% to 97% of products as unhealthy. Content analyses track the prevalence of child-targeted techniques (cartoon characters as the most frequently measured), while other studies assess their effectiveness. Overall, this scoping review offers important insights into the differences between techniques tracked and those measured for effectiveness in existing literature, and identifies gaps for future research around the question of persuasive power-particularly when it comes to children's age and the specific types of techniques examined.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".