Development of a quantitative food frequency questionnaire for use among rural <scp>S</scp>outh <scp>A</scp>fricans in <scp>K</scp>wa<scp>Z</scp>ulu‐<scp>N</scp>atal
Bibliographic record
Abstract
BACKGROUND: South Africa is experiencing a dietary and lifestyle transition as well as increased rates of noncommunicable chronic diseases. Limited information is available on the diets of rural populations. The present study aimed to characterise the diets of men and women from rural KwaZulu-Natal (KZN) and develop a quantitative food-frequency questionnaire (QFFQ) specific for this population. METHODS: A cross-sectional study was carried out by collecting single 24-h dietary recalls from 81 adults and developing a QFFQ in Empangeni, KZN, South Africa. RESULTS: The diet of this population was limited in variety, high in plant-based foods (especially cereals and beans), and low in animal products, vegetables and fruits. Amaize meal staple (Phutu) was consumed by over 80% of subjects and accounted for almost 45% of energy intake, as well as making an important contribution to fat and protein intake. Most of the protein consumed by the study population was plant-based protein, with almost 40% being obtained from the consumption of phutu and beans. A culturally appropriate QFFQ was developed that includes 71 food and drink items, of which 16 are composite dishes unique to this population. CONCLUSIONS: Once validated, this QFFQ can be used to monitor diet-disease associations, evaluate nutritional interventions and investigate dietary changes in this population.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".