Path Analysis on the Children’s Characteristics and Environmental on the Quality of Life of Children Aged 2-4 Years
Bibliographic record
Abstract
OBJECTIVE: Quality of life is a person's self-perception of the enjoyment and satisfaction of life. Health related quality of life (HRQOL) is multidimensional, which is the individual's perception of the impact of a person's health. Data from US News shows the quality of life of Indonesia is ranked at 40 from the 80 countries in the survey. The quality of life of children can be influenced by factors such as foster patterns, immunization status, breast feeding, no smoking area, and safe water. This research aims to determine the influence of exclusive feeding, immunization, foster patterns, no smoking are, and safe water to the health status and quality of life of children. METHOD: This research is a quantitative observational study with a research design of retrospective cohort studies. Population of this study is all toddlers aged 2-4 years old who reside in the village worthy of children (exposed groups) and ordinary villages (unexposed groups) in the region of Sleman regency. The large sample in this study was 350 respondents with multistage random sampling data retrieval techniques. FINDINGS: The quality of life of the children was directly affected by health status (b=0.006; SE=0.054; p<0.001), foster pattern (b=0.079; SE=0.055; p<0.001), and safe water (b=0.004; SE=0.145; p<0.001). Health status was affected by exclusive breast feeding, foster pattern, and safe water. Foster pattern was affected by safe water (b=0.056). CONCLUSION: The quality of life of the children is directly affected by health status, foster patterns, and safe water. The quality of life is indirectly affected by exclusive breast feeding.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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) | 0.011 | 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".