Characterizing Retail Food Environments in Peri-Urban Pakistan during the COVID-19 Pandemic
Bibliographic record
Abstract
(1) Background: To date, there are limited data in low- and middle-income countries (LMICs) that collect, monitor, and evaluate food environments in standardized ways. The development of a pilot survey tool, tailored to LMICs and focused on retail food environments, is necessary for improving public health nutrition. (2) Methods: A novel survey tool was developed and piloted in a sample of village retail food environments (n = 224) in Matiari, Pakistan between October 2020 to April 2021. Villages were randomly selected, and food outlets were surveyed within a 500-m radius from each village center. Descriptive statistics (counts and percentages) were used to describe the characteristics of food outlets and the availability of food. To test whether there was a difference in characteristics or in the mean of number of healthy, unhealthy, and total food items available by village size, a χ2 test or one-way ANOVA was conducted, respectively. (3) Results: In total, 1484 food outlets were surveyed for food accessibility, availability, and promotion across small (n = 54), medium (n = 112), and large villages (n = 58). In small and medium-sized villages, mobile food vendors were the predominant food outlet type (47.8% and 45.1%, respectively), whereas in large villages, corner stores (36%) were more prominent. The mean number of total food items (p < 0.006) and unhealthy food items (p < 0.001) available in food outlets differed by village size. The proportion of food outlets with available fruits, meat and poultry, water, and sugar-sweetened beverages also differed by village size (p < 0.001). (4) Conclusions: This study informs the global evidence gap in the current understanding of food environments in various ethnically diverse and dynamic LMICs, and the developed methodology will be useful to other LMICs for measuring and monitoring the food environment, especially among vulnerable population groups. This work complements current national and provincial survey efforts in Pakistan.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".