The social determinants of health facility delivery in Ghana
Bibliographic record
Abstract
BACKGROUND: Many women still deliver outside a health facility in Ghana, often under unhygienic conditions and without skilled birth attendants. This study aims to examine the social determinants influencing the use of health facility delivery among reproductive-aged women in Ghana. METHODS: Nationally representative data from the 2014 Ghana Demographic and Health Survey was used to fit univariable and multivariable logistic regression models to estimate the influence of the social determinants on health facility delivery. Andresen's health care utilization model was used as the conceptual framework guiding this study.. RESULTS: Only 72% of deliveries take place at a health facility in Ghana. The results of the adjusted model indicate that place of residence, financial status, education, religion, parity and perceived need were significantly associated with health facility delivery. First, urban women had a higher likelihood of health facility delivery than rural women (Adjusted Odds ratio [AOR] =2.21; 95% Confidence interval [CI] = 1.53-3.19). Second, middle-class and rich women were 1.57 (95%CI = 1.18-2.08) times and 6.91 (95%CI = 4.12-11.59) times, respectively more likely to deliver at health facility compared to the poor. Third, women with either at least secondary education (AOR = 2.04; 95%CI = 1.57-2.64) or primary education (AOR = 1.39, 95%CI = 1.02-1.92) were more likely to deliver at health facility than women with no education. In terms of parity, first time mothers were 1.58 (95% CI = 1.18-2.12) times more likely to deliver at health facility than those who had given birth three or more times before. Finally, regarding perceived need, women who were aware of pregnancy complications were 1.32 (95%CI = 1.02-1.70) times more likely to use health facility delivery than those who were not informed about pregnancy complications. CONCLUSIONS: First, in spite of Ghana's free maternal health services policy, poorer women were much less likely to have a health facility delivery, which points to the need to understand the indirect costs and other financial barriers preventing women from delivering at a health facility. Second, many of the identified variables influence the demand and not just the supply for health care services, and highlight the importance of the social determinants of health and investments in interventions that extend beyond improving physical access.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".