Predictors of Child’s Health in Pakistan and the Moderating Role of Birth Spacing
Bibliographic record
Abstract
There is a consensus that better health should be viewed both as a means and an end to achieve development. The level of development should be judged by the health status of the population and the fair distribution of health services across the people. Many determinants affect a child's health. This study aimed to explore a child's health predictors and the moderating role of birth spacing on the association between mother's health care services utilization (MHCSU) and a child's health. In this study, we used the dataset of Pakistan Demographic and Health Survey 2017-18 to explore the predictors of child health and the moderating role of birth spacing through binary logistic regression, using SPSS version 20. The results showed an association of mother's age (35 to 49 years), her education (at least secondary), health care services (more accessible), father's education (at least secondary), their wealth status (high), and exposure to mass media to improved child health. However, the effect of a mother's employment status (employed) on her child's health is significant and negative. The coefficient of moderation term indicated that the moderating role of birth spacing on the association between MHCSU and a child's health is positive. We conclude that birth spacing is a strong predictor for improving a child's health. The association between MHCSU and child's health is more distinct and positive when the birth spacing is at least 33 months.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".