Risk factors for Caesarean sections in Ghana: evidence from the Ghana Maternal Health Survey
Bibliographic record
Abstract
A Caesarean section can be a life-saving intervention in case of pregnancy complications or difficult labour. The prevalence of Caesarean section continues to increase, especially in sub-Saharan Africa, yet the reasons for this remain largely unexplored. This study investigated risk factors contributing to the decision to perform Caesarean sections in Ghana using data from 8645 women aged 15-49 years from the 2017 Ghana Maternal Health Survey. The data were analysed by applying complementary log-log and logit models. The majority of Ghanaian women (about 87%) reported preferring vaginal delivery to Caesarean section. Of those who had undergone a Caesarean section for their most recent birth, about 55% had an elective rather than an emergency section. Women with labour complications (prolonged/obstructed labour) were significantly more likely to have a Caesarean section (OR=4.09, 95% CI=3.10-5.41). Furthermore, women with maternal complications, particularly prolonged/obstructed labour, were less likely to have an elective Caesarean section than those who had no such complications (OR=0.25, 95% CI=0.14-0.46). Compared with poorer women, wealthy women were significantly more likely to have an elective Caesarean section (OR=1.84, 95% CI=1.08-3.14). The findings suggest that beyond maternal complications, women's socioeconomic and demographic characteristics are important risk factors for undergoing a Caesarean section in Ghana.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".