Implication of Natal Care and Maternity Leave on Child Morbidity: Evidence from Ghana
Bibliographic record
Abstract
The aim of government with the help of the Ghana Health Service (GHS) and other stakeholders has been to reduce the level of child morbidity which leads to child mortality in Ghana. This study on natal care and its implication on child morbidity would help the government in formulating appropriate policies to curb this problem. This study uses Acute Respiratory Infection (ARI) which is an infection of the lungs and respiratory tract as a proxy for child morbidity. The specific aim of this study is to ascertain the effect of Natal Care (Antenatal care, Delivery care and Post-natal care) and Maternity leave on Child Morbidity. The study employed data from the Ghana Demographic and Health Survey (2014) using the Probit estimation method to estimate the health, demographic and income factors that influence child morbidity in Ghana. It shows evidence that some stages of natal care, unpaid maternity leave, and other demographic factors have a significant impact on child morbidity in Ghana. Specifically, failure to receive post-natal care within first week of delivery causes a 3% increase in the possibility of ARI in children under five. The study also shows that a mother’s income determines her health care purchases; in that an unpaid maternity leave causes a 3.9% increase in the possibility of ARI in children under five compared to a paid maternity leave.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".