Food marketing and gender among children and adolescents: a scoping review
Bibliographic record
Abstract
BACKGROUND: Pervasive marketing of unhealthy foods is a contributing factor to the growth of the global epidemic of childhood and adolescent overweight and obesity. Sex and gender differences come into play in the design of and responses to these marketing strategies, contributing to the perpetuation of stereotyped behavior and generating disparities in food choices and health. The purpose of this paper is to review the current literature regarding gender differences in food marketing design and perception among children and adolescents to facilitate evidence-based policy dialogues to address gender-based health disparities in NCD prevention. METHODS: Scoping review of articles published in scientific journals in English and Spanish, from 2003 to 2018, that addressed the influence of food marketing among children and adolescents including a gender perspective. The methodological quality of each article was assessed following criteria specific to each study design. RESULTS: From a total of 37 articles (39 studies) included in the review, 17 were experimental and 22 had descriptive, cross-sectional designs. Twenty-one studies were found to have low methodological quality, while 10 and 8 were of medium and high quality, respectively. A total of 23 studies among children and adolescents found gender-based differences. Differences were found in the following dimensions: food marketing on intake; responses to specific marketing; perceptions and attitudes towards food marketing and marketing regulation initiatives; exposure to food advertising and gendered marketing content. The evidence was not conclusive in any of the dimensions. CONCLUSIONS: The evidence suggests that food marketing has a similar impact on the consumption of unhealthy foods on boys and girls, but boys were found to be exposed to food advertising more intensively and their preferences to be more affected by this exposure, coinciding with a male-dominant advertising content. Limitations of these studies include taking gender as an unproblematic construct equivalent to biological sex and the lack of studies focused on developing countries. As gender is a cross-sectional dimension that interacts with other factors driving health disparities, an integrated gender perspective is needed to develop effective, evidence-based policies to control food marketing and tackle the childhood overweight and obesity pandemic.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".