Development and testing of two tools to assess point-of-sale food and beverage marketing to children in restaurants
Bibliographic record
Abstract
OBJECTIVE: To describe the development and testing of two assessment tools designed to assess exterior (including drive-thru) and interior food and beverage marketing in restaurants with a focus on marketing to children and teens. DESIGN: A scoping review on restaurant marketing to children was undertaken, followed by expert and government consultations to produce a draft assessment tool. The draft tool was mounted online and further refined into two separate tools: the Canadian Marketing Assessment Tool for Restaurants (CMAT-R) and the CMAT-Photo Coding Tool (CMAT-PCT). The tools were tested to assess inter-rater reliability using Cohen's Kappa and per cent agreement for dichotomous variables, and intra-class correlation coefficients (ICCs) for continuous or rank-order variables. SETTING: Waterloo, Ontario, Canada. PARTICIPANTS: 57), and thirty randomly selected photos were coded using the CMAT-PCT. RESULTS: The CMAT-R collected data on general promotions and restaurant features, drive-thru features, the children's menu and the dollar/value menu. The CMAT-PCT collected data on advertisement features, features considered appealing to children and teens, and characters. The inter-rater reliability of the CMAT-R tool was strong (mean per cent agreement was 92·4 %, mean Cohen's κ = 0·82 for all dichotomous variables and mean ICC = 0·961 for continuous/count variables). The mean per cent agreement for the CMAT-PCT across items was 97·3 %, and mean Cohen's κ across items was 0·91, indicating very strong inter-rater reliability. CONCLUSIONS: The tools assess restaurant food and beverage marketing. Both showed high inter-rater reliability and can be adapted to better suit other contexts.
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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 | 0.070 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".