Store Environment Assessment (SEA) Tool: Design and validation of a food environment audit protocol
Bibliographic record
Abstract
Abstract Background Unhealthy food environments are key factors in diet-related disease risk. Many audit methods have been designed for the retail food environment, but weaknesses of existing methods include: validity in study methods and design, and heterogeneity in adaptation and analysis methods. This paper describes the development and validation of a novel protocol for designing store audit instruments - the Store Environment Assessment (SEA) Tool. Methods This research involved four steps: 1. a scoping review of consumer food environment audit tools; 2. classify key variables from the literature; 3. use the variables to design a protocol for development of audit instruments tailored to local contexts and research questions; and 4. validate the protocol by designing and validating a sample audit tool based on Canada's Food Guide (2019). Results Variables from the literature included: Product (availability, variety, size, reference), Price, Promotion, Placement-accompanied by: definition, type of variable, range of values, scale of measurement, and measurement outcome. The protocol has seven steps: identify dietary guideline criteria; conduct content analysis; define setting; align research questions with scale and scope; variable selection; audit tool design; and validation strategy. The protocol was used to design a store audit instrument for Canadian jurisdictions, based on Canada's Food Guide, and validated against the gold standard, Nutrition Environment Measures Survey (NEMS). Discussion The SEA protocol can strengthen researchers and practitioners' capacity to use structured guidelines to develop geographically and socio-demographically relevant Store Environment Assessments, and avoid heterogeneity arising from ad hoc adaptations of tools such as NEMS. Our methodological approach can support greater consistency, feasibility, and rigour of food environment audits for diverse public health research and practice objectives. Key messages Researchers and practitioners will be able to utilize the SEA tool protocol, using jurisdictional food and nutrition criteria, to better assess 4Ps related to food, in retail food environments. The variables and measurement outcomes included in the SEA tool protocol will support public health practitioners in developing relevant interventions to support healthy consumer food purchasing.
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 | 0.362 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".