Development and evaluation of food environment audit instrument: AUDITNOVA
Bibliographic record
Abstract
OBJECTIVE: To develop and assess the reliability of an instrument that enables auditing information on consumer food environment indicators, such as availability, price, promotional and advertising strategies, and quantity of brands available, using the food recommendations adopted by the Dietary Guidelines for the Brazilian Population as a theoretical basis. METHODS: This is a methodological study in two phases: 1. development of the audit instrument and 2. assessment of its reliability and reproducibility . The Content Validity Index was estimated for each instrument item (>0.80 satisfactory). Inter-rater and test-retest reliability were assessed by percentage agreement and Kappa coefficients. Pearson's correlation coefficient and Scatter-plots were used to measure the degree of linear correlation between two quantitative variables. RESULTS: The Content Validity Index was 0.91. Inter-rater and test-retest reliability were mostly high (Kappa> 0.80), for food availability indicators. Among the items that measure advertising, Kappa values for inter-rater reliability ranged from 0.57 to 1.00 and for the test-retest ranged from 0.18 to 0.90. Prices and quantity of brands showed a positive linear correlation between measurements performed by researcher 1 and 2 and between visits 1 and 2. CONCLUSIONS: AUDITNOVA is reliable for measuring aspects such as availability, price, quantity of brands, and advertising of foods available in the consumer food environment.
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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 | 0.035 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".