Vegetables and fruits retailers in two urban areas of Bangladesh: Disruption due to COVID– 19 and implications for NCDs
Bibliographic record
Abstract
Bangladesh is experiencing an increasing prevalence of diet-related non-communicable diseases (NCDs). Considering daily total requirement of 5 servings as minimum recommended amount, 95.7% of people do not consume adequate fruit or vegetables on an average day in the country. Imposition of lockdown during COVID-19 created disturbance in fresh fruits and vegetable production and their retailing. This incident can make these dietary products less affordable by stimulating price and trigger NCDs. However, little is known about the supply chain actors of healthy foods such as vegetables and fruits in urban areas, and how they were affected due to pandemic. Aiming toward the impact of COVID-19 on the business practices and outcomes for the vegetables and fruits retailers in Bangladesh, a survey of 1,319 retailers was conducted in two urban areas, namely Dhaka and Manikganj from September 2021 to October 2021. To comprehend the impact of COVID-19 on the profit margin of the retailers and on the percentage change in sales, a logistic and an Ordinary Least Squares (OLS) regression were estimated. Significant difference in the weekly business days and daily business operations was observed. The average daily sales were estimated to have a 42% reduction in comparison to pre-COVID level. The daily average profit margin on sales was reportedly reduced to 17% from an average level of 21% in the normal period. Nevertheless, this impact is estimated to be disproportionate to the product type and subject to business location. The probability of facing a reduction in profit margin is higher for the fruit sellers than the vegetable sellers. Contemplating the business location, the retailers in Manikganj (a small city) faced an average of 19 percentage points less reduction in their sales than those in Dhaka (a large city). Area-specific and product-specific intervention are required for minimizing the vulnerability of retailers of vegetables and fruits and ensuring smooth supply of fruits and vegetables and increasing their uptake to combat diet related NCD.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".