The association between diet price and diet quality among Australian adults participating in the 2020 International Food Policy Study
Bibliographic record
Abstract
Abstract This cross-sectional study examined the association between diet price and diet quality in a national sample of Australian adults ( n 1956). Diet recall data from the 2020 International Food Policy Study were linked to a national food and beverage price dataset. Daily diet price was calculated by summing the median non-promotional prices of all foods and beverages recorded in diet recall data, priced per gram (or millilitre) and adjusted for edible portions. Diet quality was determined using the Australian Dietary Guideline Index 2013 (scored out of 115). Linear regression models tested the association between the diet price (per dollar and per ten-dollar increments) and diet quality, adjusted for education, age and sex. A positive association was observed, where diet quality increased by 0·09 units (95 % CI 0·05, 0·14) for every $AU 1 increase in diet price. Daily diet price explained approximately 8 % of the variation in diet quality across the sample ( R 2 = 0·08). When categorised in ten-dollar increments, participants with diet prices < $AU 10/d had a lower mean diet quality score (51·96) compared with all other diet price categories, 5–6 points lower than those whose diet was > $30/d. Diet price appeared to be a modest yet significant determinant of diet quality for Australian adults in 2020. Additional analyses are needed to investigate these associations during recent food inflation. As diet quality appears to be lowest for people who spend the least on food, government action to increase priority communities’ food budgets may help improve the nutritional quality of population diets.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".