A Descriptive Survey to Analyze the Dietary Changes among the Indian Population during the COVID-19 Pandemic
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».