Trends in Nutritional Status of School Children in Eastern Nepal: A Longitudinal Assessment over the Five Years (2017-2021)
Bibliographic record
Abstract
Introduction/Background: Nutritional screening is a vital tool for assessing the health and well-being of school children, as it provides crucial data on their nutritional status. Longitudinal assessments allow for the monitoring of nutritional trends over time and facilitate the development of targeted dietary goals. Aim/Objective: This study evaluated the longitudinal nutritional status of children enrolled at YouMe School, located in the rural Khotang district of Eastern Nepal. Methodology: The research team deployed a study design with open recruitment. The team recorded one or more observations per individual. Annual nutritional screenings were systematically conducted from 2017 to 2021, covering students from Nursery to Grade 7. The primary outcome variables included weight-for-height Z-scores (WHZ) and Body Mass Index Z-scores (BMI-Z). Data analysis involved linear mixed models adjusted for potential confounders. Results: One thousand thirty-two observations were recorded over five years, comprising students from Nursery to Grade 7. The findings revealed progressive improvements in nutritional status, demonstrated by increases in weight-for-age z-scores (from -1.1 in 2017 to -0.6 in 2021), BMI z-scores (from -1.4 to -0.4), and weight-for-height z-scores (from -1.3 to -0.08) among children at YouMe School. Adjusted analyses confirmed significant improvements in both WHZ and BMI Z-scores over time. Additionally, the gender disparity in nutritional status decreased substantially over time (β=-0.01, p<0.01). Conclusion: Five consecutive years of nutritional screenings at YouMe School in Eastern Nepal enabled an assessment of student nutritional well-being from Nursery to Grade 7. The school's sustained initiatives likely contributed to improved nutritional outcomes and reduced gender disparities. An integration of educational programs, targeted nutritional initiatives, and a supportive school environment can expedite sustainable dietary gains.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".