Prevalence of stunning and associated factors among primary school children of lafoole erigavo sanag Somalia in 2023
Bibliographic record
Abstract
ABSTRACT stunning is a measure of acute under nutrition and it may result from inadequate food intake or from a recent episode of illness that caused weight loss Stunting is a sign of chronic under nutrition that reflects failure to receive adequate nutrition over a long period and can also be affected by recurrent and chronic illness Under nutrition in children occurs due to the interplay of several factors, which include variables related to the maternal age, maternal education, poor feeding practice, maternal nutritional status, parity and multiple births, sex of the child, illness, birth interval immunization status, poor wealth status, large families, water and sanitation Factors has become a major public health problem especially in low resource settings there is a scarcity of evidence regarding prevalence and associated risk factors among school children in our study area. The aim of this study was to assess the prevalence of stunning and associated factors among in primary school’s children of lafoole erigavo sanag Somalia. This study shed light on the lafole primary and its prevention and control methods in Somalia.Data were collected by using a pre-tested structured questionnaire that was adopted form different literatures and it was translated into the local language (Somali version) furthermore it was translated back to English to check the consistency of the questionnaire. Respondents were parents/caregivers of the children identified in the study schools. After students were systematically selected from the schools, their household addresses were traced in the student’s parent database. Then data collectors went to the children’s house to interview parents/caretakers. Then data was exported to SPSS version 23 and for anthropometric data WHO Anthos+ was used to determine stunting and then analysis was done by SPSS 23. First descriptive analyses were done to explore the socio-demographic characteristics of the respondents. Factors for which significance bivariate association was observed and variables which yield p-value of <0.25 retained for subsequent analyses by using multiple logistic regressions to identify the associated factors of school child stunting. Variables which were less than 0.05 were taken subsequently on the second model.
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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 | 0.000 |
| 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) | 0.002 | 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".