Women’s Autonomy in Maternal Healthcare Decision-Making in Urban Ghana
Bibliographic record
Abstract
Enhancing women’s decision-making autonomy in developing countries constitutes one of the recognised approaches to improving maternal healthcare service utilisation. The inability of women to make decisions about their health, the lack of universal health insurance, and inadequate health facilities are contributing factors to high maternal mortality rates in many countries in the developing world. This study explored women’s decision-making autonomy over maternal healthcare in Ghana. The authors used a mixed method design, collecting quantitative data through a survey of 163 pregnant and lactating mothers from private and public health centres in Madina, a suburb of Accra in the Greater Accra Region. They also gathered qualitative data from four nurses/midwives and 40 women and their partners. The study identified a clear dominance of men over women in making maternal health decisions, explained mainly by cultural, financial and religious factors. It also identified two other decision-making processes influenced by economic factors: a balanced or democratic decision-making process and a women-dominated decision-making process. The paper concludes that there is a need for a change in cultural norms and stereotypes, particularly concerning the supply side of health services and the factors driving individuals to seek quality and appropriate maternal health care. Presently, these decisions are heavily influenced by cultural and economic patriarchal relations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".