A Descriptive Survey to Analyze the Dietary Changes among the Indian Population during the COVID-19 Pandemic
Bibliographic record
Abstract
AbstractAim: The aim of the present study was to analyze the dietary habits and consumption of different food groupsduring the pandemic among the Indian population and to observe if there is any shift toward or away from thebalanced diet.Methods: A cross‑sectional study was conducted among 500 participants between the age group of 18 and 60years through an online survey among the general population for a period of 1 month during the secondlockdown for COVID‑19. Participants were ensured of confidentiality and that no information was shared to anythird party during and after the completion of the study. Permission from the Institutional Ethics Committee wasobtained prior to conduction of the study.Results: Majority of the respondents belonged to the age group of 18‑30 years. A large number of participantswere females and undergraduate students. Metropolitan cities were observed to have highest number ofrespondents in our study It was observed that majority participants had no change in consumption and almost30% had raised their consumption of poultry. On the other hand, 17% reduced their intake during the pandemic.It was observed that a large number of respondents increased and less than half of respondents had no change inconsumption of milk, whereas 12% decreased drinking it during the lockdown. A large number of participantsturned up their consumption and 40% participants had no change in the consumption of breads and buns. Incontrast, only 18% turned down their consumption during the pandemic. Change in consumption of fruits andvegetables. It was observed that almost two thirds of participants turned up their fluids consumption. In contrast,one third participants had no change and only a few participants turned down their consumption of water duringthe pandemic. Maximum difference was observed in case of intake of carbonated beverages where intake waslowered.Conclusion: There was a paradigm shift in consumption of certain products primarily to boost immunity andfight the COVID‑19 pandemic. Majority of the participants have increased consumption of healthy foods likemilk, fruits, vegetables, and nuts which is the need of the hour given that immunity has a big role to play infighting against COVID‑19
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".