Reliability and validity of a novel tool to comprehensively assess food and beverage marketing in recreational sport settings
Bibliographic record
Abstract
BACKGROUND: Current methods for evaluating food marketing to children often study a single marketing channel or approach. As the World Health Organization urges the removal of unhealthy food marketing in children's settings, methods that comprehensively explore the exposure and power of food marketing within a setting from multiple marketing channels and approaches are needed. The purpose of this study was to test the inter-rater reliability and the validity of a novel settings-based food marketing audit tool. METHODS: The Food and beverage Marketing Assessment Tool for Settings (FoodMATS) was developed and its psychometric properties evaluated in five public recreation and sport facilities (sites) and subsequently used in 51 sites across Canada for a cross-sectional analysis of food marketing. Raters recorded the count of food marketing occasions, presence of child-targeted and sports-related marketing techniques, and the physical size of marketing occasions. Marketing occasions were classified by healthfulness. Inter-rater reliability was tested using Cohen's kappa (κ) and intra-class correlations (ICC). FoodMATS scores for each site were calculated using an algorithm that represented the theoretical impact of the marketing environment on food preferences, purchases, and consumption. Higher FoodMATS scores represented sites with higher exposure to, and more powerful (unhealthy, child-targeted, sports-related, large) food marketing. Validity of the scoring algorithm was tested through (1) Pearson's correlations between FoodMATS scores and facility sponsorship dollars, and (2) sequential multiple regression for predicting "Least Healthy" food sales from FoodMATS scores. RESULTS: Inter-rater reliability was very good to excellent (κ = 0.88-1.00, p < 0.001; ICC = 0.97, p < 0.001). There was a strong positive correlation between FoodMATS scores and food sponsorship dollars, after controlling for facility size (r = 0.86, p < 0.001). The FoodMATS score explained 14% of the variability in "Least Healthy" concession sales (p = 0.012) and 24% of the variability total concession and vending "Least Healthy" food sales (p = 0.003). CONCLUSIONS: FoodMATS has high inter-rater reliability and good validity. As the first validated tool to evaluate the exposure and power of food marketing in recreation facilities, the FoodMATS provides a novel means to comprehensively track changes in food marketing environments that can assist in developing and monitoring the impact of policies and interventions.
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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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".