Using different approaches to assess the reproducibility of a culturally sensitive quantified food frequency questionnaire
Bibliographic record
Abstract
Objective: To report on the use of different approaches to assess the reproducibility of a culturally sensitive quantified food frequency questionnaire (QFFQ) used for assessment of the habitual dietary intake of Setswana-speaking adults in the North West Province of South Africa.Method: A previously developed and validated QFFQ was completed by trained fieldworkers. Portion sizes were estimated using different methods. Food intake was coded and analysed for nutrient intake per day for each subject. The first interview (n = 1 888) took place during the baseline data collection period. For the second interview (n = 175), a convenient sample from the subjects who had completed the first interview was collected and the interview was conducted within four to six weeks of the first interview.Results: There were good correlations between the first and second QFFQ for all the nutrients (p < 0.0001). The Wilcoxon signed-rank test showed that there were no significant differences in the median intake between the two administrations, except for energy and total fat. The Bland-Altman plots showed good agreement. Between 41% and 58% of the subjects were correctly classified into the same quartile, with less than 3% grossly misclassified. The weighted κ statistics showed moderate agreement between the two applications.Conclusion: Our results show that more than one statistical approach is needed to assess the reproducibility of a QFFQ. The reproducibility of this culturally sensitive QFFQ was good.
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 | 0.088 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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".