Acute food insecurity and short-term coping strategies of urban and rural households of Bangladesh during the lockdown period of COVID-19 pandemic of 2020: report of a cross-sectional survey
Bibliographic record
Abstract
INTRODUCTION: We conducted a cross-sectional survey to assess the extent and to identify the determinants of food insecurity and coping strategies in urban and rural households of Bangladesh during the month-long, COVID-19 lockdown period. SETTING: Selected urban and rural areas of Bangladesh. PARTICIPANTS: 106 urban and 106 rural households. OUTCOME VARIABLES AND METHOD: Household food insecurity status and the types of coping strategies were the outcome variables for the analyses. Multinomial logistic regression analyses were done to identify the determinants. RESULTS: We found that around 90% of the households were suffering from different grades of food insecurity. Severe food insecurity was higher in urban (42%) than rural (15%) households. The rural households with mild/moderate food insecurity adopted either financial (27%) or both financial and food compromised (32%) coping strategies, but 61% of urban mild/moderate food insecure households applied both forms of coping strategies. Similarly, nearly 90% of severely food insecure households implemented both types of coping strategies. Living in poorest households was significantly associated (p value <0.05) with mild/moderate (regression coefficient, β: 15.13, 95% CI 14.43 to 15.82), and severe food insecurity (β: 16.28, 95% CI 15.58 to 16.97). The statistically significant (p <0.05) determinants of both food compromised and financial coping strategies were living in urban areas (β: 1.8, 95% CI 0.44 to 3.09), living in poorest (β: 2.7, 95% CI 1 to 4.45), poorer (β: 2.6, 95% CI 0.75 to 4.4) and even in the richer (β: 1.6, 95% CI 0.2 to 2.9) households and age of the respondent (β: 0.1, 95% CI 0.02 to 0.21). CONCLUSION: Both urban and rural households suffered from moderate to severe food insecurity during the month-long lockdown period in Bangladesh. But, poorest, poorer and even the richer households adopted different coping strategies that might result in long-term economic and nutritional consequences.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".