Stunting Diagnostic and Awareness: Impact assessment study of sociodemographic factors of stunting among school-going children of Pakistan
Bibliographic record
Abstract
Abstract Background: Stunting is a major public health issue in most of developing countries. Although, its worldwide prevalence is decreasing slowly but the number of stunted children is still rising in Pakistan. Stunting is highly associated with several long-term consequences, including higher rate of mortality and morbidity, deficient cognitive growth, school performance, learning capacity, work capacity and work productivity. To prevent stunting, we proposed Stunting Diagnostic and Education app. This app includes detailed knowledge of stunting and it’s all forms, symptoms, causes, video tutorials and guidelines by the Pediatricians and Nutritionists.Methods: A cross-sectional study has been conducted in schools of Multan District, Pakistan for the period of January 2019 to June 2019. Sample data of 1420 children, aged 4 to 18 years using three age groups, were analyzed by using SPSS version 21.0 to assess the prevalence of stunting and to analyze the risk factors associated with it in children under and over 5 age. Chi square test was applied in comparison with rural and urban participants and p-value <0.05 was considered as significant. This study includes distribution of sociodemographic characteristics, parental education, working status of mothers, dietary patterns of school going children and prevalence of stunting in school going children. After getting study results, Stunting Diagnostic and Education app was developed according to the instructions of child experts and nutritionists.Results: 354 (24.93%) participants were stunted out of 1420, 11.9 % children were obese and 63.17% children were normal. Out of 354 stunted children, higher ratio of stunting was found in the age group of 8-11 years children with 51.98 percentage. 37.85% stunted children were found in the age group of 4-7 years and 10.17% stunting was found in the age group of 12-18 years children. It was observed in the study that male children were highly stunted than female with 57.91 % and 42.09% respectively. Children living in rural areas were more stunted affected as compared to the children living in urban society with percentage 58.76 and 41.24 respectively.Conclusions: Our study concluded that 24.93% children were stunted, out of which, age group of 8-11 years children were highly stunted. The study showed that the literacy of mother or caregiver had high impact on children’s health. Therefore, Stunting Diagnostic and Education app was developed to educate mothers to prevent stunting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".