How is food variety conceptualised and measured as a diet quality indicator in developed settings? Protocol for a systematic scoping review
Bibliographic record
Abstract
OBJECTIVES: Dietary diversity (DD) is a pillar of healthy eating guidance and can be used to assess diet quality. Despite being an established nutrition concept, many inconsistencies in its definition and measurement exist and meanings vary across the development spectrum. This protocol outlines a research trajectory, whereby a scoping review will be undertaken to illustrate and map the methodological approaches that have been utilised to measure diversity as a marker of diet quality in the general population. It seeks to determine the most common and less used methodological approaches to measure DD in the diet of healthy adults. METHODS AND ANALYSIS: Scoping review of peer-reviewed and grey literature from five bibliographic databases, supplemented by handsearching of reviews and reference lists. Search terms will include DD, food variety, mixed diet, balanced diet and food group variety. Eligible articles must include a measure for DD as an indicator of diet quality in the general population living in developed settings. Two independent reviewers will screen titles or abstracts, and read full-texts. Consensus will resolve any disagreements on study eligibility with a third reviewer consulted if needed. Data will be extracted using a standardised evidence table and analysed using a narrative synthesis approach. Data will be managed using Covidence. ETHICS AND DISSEMINATION: No ethics is required for this study using public documents. Results will be disseminated through peer-reviewed papers and scientific conferences. DISCUSSION: This scoping review will help to map, classify and assess the methodological approaches used in the nutrition literature to measure DD as a diet quality indicator. We anticipate a wide range of DD measures and expect to identify the most prevalent DD measures used to assess diet quality. Our findings will inform standardisation to improve future research on this nutritional concept.
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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 | 0.194 | 0.241 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.080 | 0.019 |
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".