The effects of individual and community-level factors on maternal health outcomes in Ghana
Bibliographic record
Abstract
BACKGROUND: Utilization of maternal health care services is key to reducing the number of perinatal deaths and post-natal complications in sub-Saharan Africa. With a few exceptions, many studies that examine the use of maternal health services in sub-Saharan Africa have focused largely on individual-level explanations and have ignored the importance of contextual and community-level explanations. In Ghana, progress has been made in reducing maternal mortality ratio from 740/100,000 in the late 1990s to 319/100,000 in 2015 but these rates are still high. Our study focuses on impact of individual and community level-factors on maternal outcomes with the hope that it will inform public policy in Ghana. This approach highlights latent or unacknowledged aspects of fragility within health systems designed to improve maternal health and opportunities for improving uptake of services. METHODS AND FINDINGS: Using the 2014 Ghana Demographic and Health Survey, we examined the effects of individual and community-level factors on antenatal care, facility-based delivery, and post-natal care. Multilevel logistic regression models were used to examine the effects of individual and community-level factors on the outcome variables. Our analysis revealed that overall utilization of antenatal, facility-based delivery and post-natal care was substantial across the board; however, both individual and community-level factors were significant predictors of these maternal health outcomes. Wealthier and better educated women were more likely to use antenatal services and facility-based delivery; in contrast poor and uneducated women were more likely to use antenatal and postnatal care but not facility-based delivery. Additionally, use of National Health Insurance Scheme was statistically associated with the utilization of maternal health services. CONCLUSIONS: The findings point to areas where services can be better tailored to meet community-specific needs. Policy makers must consider factors such as educational levels and economic security at both individual and community-levels that shape women's preferences and uptake of maternal health care in Ghana.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".