Malnutrition and Associated Factors with Nutritional Status among Orphan Children: An Evidence-Based Study from Nepal
Bibliographic record
Abstract
Abstract Background: Malnutrition is common public health problem among children in low- and middle-income countries. Orphan’s children are vulnerable and neglected group in the society and are more prone to malnutrition. The study aims to identify prevalence of underweight, stunting and thinness of children residing in slum area with its associated factors. Method: Quantitative method & analytical cross-sectional research design were used to assess the nutritional status and its associated factors among orphan children in Pokhara, Nepal. The sample size of 160 children were obtained by simple random technique. Semi-structured questionnaire, digital bathroom scale, stadiometer was used as data collection technique. Data management and analysis was done from Epi-info, SPSS 25 version & WHO Anthro plus. Result: Out of 160 children, majority 80% were malnourished & 55.1% were stunted whereas 13.8% were thinness & 6.9% were found to be overweight. Our study revealed a significant association between sex, age, ethnicity, duration of stay in children’s home and education of caregiver (p <0.05). Conclusion: Malnutrition is highly prevalent in orphan children and need to be addressed. Nutritional status should be monitored regularly for early identification & timely intervention of malnutrition to promote the nutrition health status of children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".