Prevalence of Stunting among Children under Five in Pediatric Hospital Al-Hasahisa, Al Jazirah State, Sudan
Bibliographic record
Abstract
A cross-sectional descriptive design study was conducted in the pediatric hospital in Al-Hasahisa-Algazera state -Sudan, to ascertain the prevalence of stunting in children under five years old. There are 100 patients in the samples (46 girls and 54 males). The questionnaire used to collect the study's primary data includes questions about participant content, sociodemographics, housing status, health, and nutrition. To estimate the patient's percentage of stunting, anthropometric measures, including height/length for age and mid-upper arm circumference (MUAC), were also obtained. The data showed that most of the children were raised in nuclear homes. A significant correlation (P value <0.05) was seen between stunting and mother education. The parents' educational attainment showed that a sizable percentage of fathers (52%) and mothers (45%) were illiterate. According to the findings, 82% of mothers were housewives, and 75% of fathers were employed. This finding demonstrated a significant (P value 0.004) relationship between income level and stunting, with weak income (22%), moderate-income (78%), and no income level. Furthermore, 36% of completed breastfeeders and 37% of non-completers showed a highly significant (P value 0.003) correlation between breastfeeding and stunting. During the course of the study, diarrheal diseases affected 68% of the children, while anemia (27%), malaria (52%), parasitic infections (17%), and chest infections (69%)were the most common conditions. 39% of the kids had severe malnutrition, and 18% had moderate malnutrition. The bulk of them, 78%, were stunted, with the remaining 19% mild, 11% moderate, and 48% severely stunted. According to the report, 35% of stunted children were female, and 43% of stunted children were male.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".