Dietary Diversity and its Associated Factors among Children Aged 6-59 Months in Madhyapur Thimi Municipality, Nepal
Bibliographic record
Abstract
Background: Minimum dietary diversity for children (MDD-C) is a benchmark developed by the World Health Organization (WHO) to assess diet diversity in infants and young children worldwide. The lack of such diet diversity among growing children, which often leads to malnutrition, has been considered a significant public health concern in Nepal. This study is to assess the dietary diversity and associated factors among children aged 6-59 months in wards 2 and 3 of Madhyapur Thimi Municipality. Methods: A cross-sectional descriptive study was carried out by measuring the research variables in 2023 among the residents of wards number 2 (Jatigaal) and 3 (Kaushaltar) of Madhyapur Thimi Municipality. The survey was created and administered by the researchers themselves. The sample size of the study was 385. The survey includes a structured questionnaire to assess dietary diversity and associated factors among children aged 6-59 months. The association between the factors was measured by using Fisher’s Exact test. Results: 73.5% of the study population fulfilled the minimum requirement of dietary diversity. Factors such as the mother’s educational status (p=0.002), mother’s ethnicity (p=0.015), monthly expenditure on food (p=0.001), awareness of communicable diseases (p=0.001), feeding times a day (p=0.001), personal hygiene practices status (p=0.004), and awareness of junk foods (p=0.001) showed a significant association with MDD-C. Conclusion: Increasing mothers’ awareness about junk food, communicable diseases, and the importance of their hygiene practices via formal or informal education campaigns is necessary to increase the proportion of children meeting the MDD-C benchmark and prevent malnutrition.
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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 | 0.001 |
| 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".