Mapping Definitions, Measures, and Methodologies for Assessing Dietary Diversity in the Nutrition Literature: Results of a Systematic Scoping Review
Bibliographic record
Abstract
Dietary diversity (DD) is an established pillar of healthy eating in dietary guidelines, but definitions, measurement, and meanings vary across settings. This scoping review aimed to clarify how DD has been conceptualized, operationalized, and measured as a healthy eating indicator, and to examine the methods and areas of improvement for research on this topic. A systematic search of peer-reviewed and gray literature was conducted using 5 bibliographic databases, organizational websites, and hand-searches addressing food variety, DD, and balanced or mixed diet in the general population in developed settings. Publications in English, French, Persian/Farsi, and Chinese were included. Extracted data were synthesized by quantitative content analysis. We identified 941 publications eligible for inclusion and randomly sampled 20% for data extraction (n = 190). Literature on DD, published since 1985, came from Asian (n = 88, 46%) and Anglo-European (n = 47, 25%) countries, mostly used food-frequency questionnaires (54%), and reported a total of 322 measures (208 assessed whole-diet diversity; 114 measured within-group diversity) and were less commonly validated (14%). Three-quarters of all measures used simple counting (n = 247) and others also weighted (n = 11) or categorized (n = 37) the counts; 25 measures calculated DD as a relative proportion. Across measures, the mean total DD score was 21.99 items or 'groups' (median, 10; range, 1-248). The 208 whole-diet DD measures were widely named and operationalized as 5-6 major food groups alone (n = 23) or in combination with subgroups or items (n = 131). Measurement of within-group diversity has grown since 2010. Over half of 114 within-group diversity measures assessed fruit and/or vegetable diversity, 25% assessed meat/alternatives diversity, 10% assessed grain diversity, and 8% assessed dairy diversity. There is wide variation in the definitions, measures, scoring methods, and foods included in nutrition literature regarding DD. To our knowledge, this is the first comprehensive, international overview of the topic, demonstrating the urgent need for standardization of DD as a research agenda to advance nutrition and food science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".