Malnutrition Incidence and Determination of Effecting Factors at 1-4 Years Old Children in Konya
Bibliographic record
Abstract
Background and Aim: Every year in the world millions of children die from malnutrition and infectious diseases. Children under the age of five are affected more quickly than other age groups from negative conditions. This study is aimed to determine the risk factors of malnutrition and incidence of malnutrition in children aged 1-4 years living in Konya. Materials and Methods: This is a cross-sectional epidemiological study. The survey about demographical information about the child and their family, child's nutrition and anthropometric measurements described by both Z-scoring and GOMEZ classification, was conducted between May-December,2016 with 1000 children aged 1-4 years in Konya province. Descriptive statistics, chi-square test, student t-test and multivariate logistic regression were performed by SPSS 18.0 considering p<0.05 as statistically significant. Results: According to the GOMEZ classification, 18.7% of children living in Konya are malnourished. According to Z-score, 3.5% (n=35) of the children were found to be underweight and 7.2% (n=72) were found as stunted. Factors affecting the malnutrition were the age range of children, the working status of the mother, the kinship status between the parents, the number of living children, maternal age, birth weight, the duration of breastfeeding, the time spent on TV/computer, the attitude and anxiety level of the mother when her child does not eat and the mother's nutrition education. Conclusion: Malnutrition is a common problem and its rate is high in Konya. It is suggested that health professionals should educate the society, especially mothers by organizing various training meetings. Moreover, it can be emphasized that health planners should prepare a program to determine malnourished children considering the risk factors of malnutrition in health screens and first step medical centers.
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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 | 0.001 | 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".