Examining and Comparing the Validity and Reproducibility of Scales to Determine the Variety of Vegetables Consumed: Validation Study
Bibliographic record
Abstract
BACKGROUND: Previous studies have reported that vegetable variety reduces the risk for noncommunicable diseases independent of the amount consumed. OBJECTIVE: This study aimed to examine and compare the validity and reproducibility of several scales to determine vegetable variety. METHODS: In total, 23 nutrition students in Japan reported their vegetable intake over the past month using a self-administered questionnaire between July and August 2021. Specifically, four scales were used: (1) a single question regarding the number of vegetables consumed (scale A); (2) a scale containing 9 vegetable subgroups included in the brief-type self-administered diet history questionnaire (scale B); (3) a scale containing 19 vegetable items included in a self-administered diet history questionnaire (scale C); and (4) a scale containing 20 vegetable items from the Ranking of Vegetable Consumers in Japan, which was analyzed based on a report on the National Health and Nutrition Survey in Japan (scale D). Scale validity was assessed by correlation with the number of vegetables consumed, which was collected from dietary records for 7 consecutive days. Reproducibility was assessed by test-retest reliability. RESULTS: Regarding the validity of the 4 scales, significant correlations were found between scales C (ρ=0.51) and D (ρ=0.44) with vegetable variety based on dietary records, but scales A (ρ=0.28) and B (ρ=0.22) were not significantly correlated. Reproducibility showed a significant correlation in scale B (ρ=0.45) and strong correlations in scales C (ρ=0.73) and D (ρ=0.75). CONCLUSIONS: The scales for vegetable items have acceptable validity and reproducibility compared to the scales that used a single question or vegetable subgroup and, therefore, may determine the variety of vegetables consumed.
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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 | 0.079 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".