Hubungan Faktor Lingkungan Fisik dan Sosial Ekonomi Keluarga Terhadap Kejadian Pneumonia Pada Balita di Wilayah Kerja Puskesmas Tahtul Yaman Kota Jambi
Bibliographic record
Abstract
Pneumonia in children including one cause of death in the world that is around 20 percent, or about 1.5 million children under five die each year from the disease. Each year there are an estimated 11-20 million children in the world were hospitalized because of pneumonia. In Indonesia, pneumonia is the leading cause of death of 13.2 percent of children under five and 12.7 percent the cause of death of children. The purpose of this study to determine the relationship between the physical environment and socio-economic families with the incidence of pneumonia in children under five in Public Health Center Tahtul Yaman Jambi City period 2015. This study is a case-control study. Retrieved 35 mothers who have children suffering from pneumonia as a case (case) and 35 mothers who have children do not suffer from pneumonia as control (control) so that the total sample of 70 respondents. The research took place in November 2016. Data was analyzed by univariate and bivariate statistical test Chi Square. Results of univariate analysis showed that patients with pneumonia mostly toddlers aged 12-23 as much as 57.1%, and pneumonia mostly male sex as much as 74.3%. The physical environment pneumonia generally unfavorable 68.6%, and a good physical environment 31.4%, the results of socioeconomic level are generally relatively high 54.3%. There is a relationship between physical environmental factors in infants, with a p-value = 0.017 and OR = 3.692 and socio-economic factors with p-value = 0.009 and OR = 5.053 with pneumonia. There is a relationship between physical environmental factors and socioeconomic families with the incidence of pneumonia in infants.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".