Households’ Socio-Economic Characteristics and Their Food Purchase Patterns: A Macro Study on IRAN Households Income and Expenditure Survey
Bibliographic record
Abstract
Dietary pattern analysis has a holistic approach to the eating behaviors of populations. The objective of this study was to identify the patterns of food expenditure (as a proxy of dietary patterns) in Iranian urban households. The study made use of Iran Households Income and Expenditure Survey data in the urban areas that included 18,701 households. Principal components analysis was used to identify major household food expenditure patterns (FEPs) and binary logistic regression models for relation between households’ socioeconomic characteristics with FEPs. Four FEPs could be identified: “Affordable and Diverse” (ADP), “Expensive and Modern” (EMP), “Traditional" (TP), and “Cheap and Western” (CWP). Factors that increased the ORs of EMP and CWP had many similarities. Being a child under 5 years old, being a child or adolescent with 6 to 18 years old and being a mother's with a university education level in the household, increased the ORs of these patterns. Also Factors that increased the ORs of ADP and TP had many similarities. Smaller family size, older family, no children under 5, and lower maternal education in the household, increased the ORs of these patterns. The ORs of " ADP" Showed no difference between income quarters rather the ORs of EMP, TP, and CWP In households in the fourth quarter of income compared to the first quarter, were 3.57 (95% CI = 3.12–4.17), 0.55 (95% CI = 0.49–0.62) and 0.59 (95% CI = 0.53–0.67) respectively. This study clearly shows the role of mother's education and the presence of children in the choice of household food expenditure pattern and in Iranian urban households, especially households with children, improving household income should be considered along with creating healthier food environments. Department of Community Nutrition, Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".