Predictors of Birth Weight Among Infants in Uasin Gishu County, Kenya
Bibliographic record
Abstract
Birth weight is a predictor of survival rate among neonates and a marker of neonatal and maternal nutrition and health. Low birth weight is associated with both short-term and long-term consequences for neonates that include risk for chronic diseases later in life. This study determined the prevalence of low birth weight as well as factors associated with birth weight in Uasin Gishu County, Kenya. This was a retrospective study in which health records of 970 mothers who delivered at Moi Teaching and Referral Hospital in Kenya were evaluated. Data were analyzed using descriptive statistics and linear regression modelling. The mean birth weight (BW) was 3.0±0.6 kg and the prevalence of low birth weight was 13.5%. The mothers’ mean age (years) was 26.0±5.8 with a median of 25 (range:14-46) years. Factors associated with low birth weight were: employment status, marital status, sex of the child, gestation, presence of deformity and pregnancy outcome. In the final multivariate linear regression model, mean infant BW reduced by 15.1% when the mother was unemployed compared to 8.7% for those who were formally employed. Infants born via CS had 9.6% higher BW than those born vaginally. BW reduced by 15.5% when the infant was female compared with those who were male. There was an increase in the infant BW by 46.0% when infant had no deformity. The relationship between the gestation period at delivery and infant BW depended on the pregnancy outcome. Factors associated with low birth weight were employment status, marital status, sex of the child, gestation period, presence of deformity and pregnancy outcome. Interventions aiming at improving BW and reducing the prevalence of LBW should consider these factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".