Validity and reliability of the Malaysian Healthy Diet Online Survey (MHDOS) for assessing diet quality in Malaysian adults.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Malaysian Healthy Diet Online Survey (MHDOS) is an online survey designed to measure diet quality of Malaysian adults. This study aimed to assess the relative validity and test-retest reliability of the MHDOS. METHODS AND STUDY DESIGN: This nationwide cross-sectional study was conducted from May to November 2022 among 218 Malaysian adults. Participants completed the MHDOS, underwent an interview-administered 24-hour diet recall (24DR), and repeated the MHDOS within two weeks. Relative validity was assessed by correlating food group servings from the MHDOS and 24DR using Spearman's cor-relation coefficients. Construct validity was evaluated by comparing Diet Score tertiles with food group servings, energy, and nutrient intakes from the 24DR. Linear trend analysis was used to compare food group and nutrient intakes across the Diet Score tertiles. Reliability was measured using the Intra-class correlation coefficient (ICC) between the initial and repeated MHDOS administrations. RESULTS: The MHDOS demonstrated moderate-to-good reliability, with ICC ranging from 0.70-0.86 for different components and 0.90 for the total Diet Score. Spearman correlation coefficients for mean food group intakes estimated from the MHDOS and 24DR ranged from 0.21-0.44 (p <0.001). Higher Diet Scores were associated with greater intake of total fibre, vitamin C, thiamine, niacin, potassium, calcium, phosphorus, and iron, as well as increased consump-tion of vegetables, fruits, and water (p-trend <0.01). CONCLUSIONS: MHDOS has good test-retest reliability and its derived Diet Score is associated with better nutrient and food group intake as estimated from 24DR. The MHDOS is a valid and reliable tool for assessing diet quality among Malaysian adults.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".