Socio-Cultural Factors Affecting Pregnancy Outcomes in the Dangme West District of Ghana
Bibliographic record
Abstract
Background: Improvement in maternal healthcare services is crucial among nations as women are more vulnerable during pregnancy, especially in developing countries with poor health systems. This study assessed the socio-cultural factors that affect pregnancy outcomes in the Dangme West District of Ghana. Methods: Qualitative methods were employed using the Dangme West District. Data was collected using key informant interviews involving health professionals in the area of maternal health care and focus group discussions with women attending antenatal clinic in the district. Results: Findings from the study indicate that most women in the district attend antenatal clinics. They prefer delivering with Traditional Birth Attendants or in prayer camps to preserve their family tradition of not using health facilities during childbirth. Pregnancy outcomes are highly influenced by cultural traditions, with pregnant women avoiding nutritious foodstuffs such as eggs, certain types of fish, fatty meat, and some vegetables due to their beliefs, a situation resulting in pregnancy-related complications such as anaemia, premature delivery, and low birth weight. Key factors affecting maternal health outcomes include poverty, poor infrastructure in the district, nutrition, religious beliefs, illiteracy, and attitude of health professionals. Conclusions: This study reveals a range of socio-cultural factors that impact directly on maternal health outcomes and which need to be targeted through appropriate public health actions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".