Duration of Prenatal Maternity Leave and Birth Weight in Ghana
Bibliographic record
Abstract
Background: Ghana has made significant progress in achieving Sustainable Development Goal 3 by reducing infant morbidity and mortality. However, more efforts are needed, particularly in addressing preterm birth and low birth weight. Work-related stress is a risk factor for both. Implementing prenatal maternity leave, paid or unpaid, may improve birth outcomes by reducing stress and exposure to unhealthy work environments. Aim: This study examines the association between the duration of prenatal leave and birth weight in Ghana. Methods: We used individual data of Ghanaian women aged 15-49 years who participated in the Ghana Demographic and Health Survey 2014. This survey uniquely included questions regarding maternity leave. The study population included 375 women who provided information on prenatal leave, birth weight, and socio-demographic factors. Logistic regression analysis was used to examine the odds of a child being above the low birth weight threshold (> 2500 g), adjusting for confounding variables. Two regressions were conducted: one using the number of days of prenatal leave, and the other comparing long versus short leave (< 30 days). Findings: A positive association was found between prenatal leave duration and newborns weighing ≥2500 g. Adjusted odds ratios were 1.03 [1.01;1.05] for each additional day of leave and 1.96 [0.99;3.88] for long versus short leave. Longer leave may allow women to focus on health and antenatal care. Conclusion: Longer prenatal leave may have a protective effect on birth weight in Ghana, where short or no leave is common, emphasizing the need for policies supporting extended prenatal leave.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".