Dietary Pattern of School-Going Children during COVID-19 Pandemic
Bibliographic record
Abstract
Background: School age is the foundation of human life. A healthy and balanced diet plays a major role in the proper growth development of the body as well as the mind. Only a balanced diet can provide all the macro and micronutrients. Covid 19 has impacted each and every human being in some or another manner. School-going children had to cope with new encounters involving online teaching-learning methodology and social distancing. As a result, these children have undergone mental and psychological trauma. This led them to follow faulty dietary habits, which would have long time impact on their health. Aim and Objectives: In the present survey, an attempt has been made to compile a report on the dietary intake of school-going children in the Roopnagar district of Punjab, India. Methodology: For the purpose sample of 100 students (7 to 12 years) was selected from four development blocks of district Roopnagar, and the cluster sampling method was adopted for data collection. A detailed pretested questionnaire on the dietary habits of children was used. Results: 59% of children were vegetarian, 32% were non-vegetarian, and 9% were eggetarian. Study findings showed that children preferred some of the items such as wheat, rice, sugar, rajma, black gram, green gram, potato, tomato, onion, apple, banana, and grapes over other food items in the same food group. Conclusion: Results showed that children do not consume a variety of food items, and some selected food items only resulting low dietary diversity and food variety.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".