Predictors of institutional delivery service utilization among women of reproductive age in Senegal: a population-based study
Bibliographic record
Abstract
BACKGROUND: In Senegal, sub-Saharan Africa, many women continue to die from pregnancy and childbirth complications. Even though health facility delivery is a key intervention to reducing maternal death, utilization is low. There is a dearth of evidence on determinants of health facility delivery in Senegal. Therefore, this study investigated the predictors of health facility-based delivery utilization in Senegal. METHODS: Data from the 2017 Senegal Continuous Survey were extracted for this study, and approximately 11,487 ever-married women aged 15-49 years participated. Chi-square test was used to select significant variables and multivariable logistic regression analysis was performed to identify statistically significant predictors at a 95% confidence interval with a 0.05 p-value using Stata version 14 software. RESULTS: Facility-based delivery utilization was 77.7% and the main predictors were maternal educational status (primary school Adjusted Odds Ratio [aOR] = 1.44, 95% CI; 1.14-1.83; secondary school aOR = 1.62, 95% CI; 1.17-2.25), husband's educational status (primary school aOR = 1.65, 95% CI; 1.24-2.20, secondary school aOR = 2.17, 95% CI; 1.52-3.10), maternal occupation (agricultural-self-employed aOR = 0.77, 95% CI; 0.62-0.96), ethnicity (Poular aOR = 0.74, 95% CI; 0.56-0.97), place of residence (rural aOR = 0.57, 95% CI; 0.43, 0.74), media exposure (yes aOR = 1.26, 95% CI; 1.02-1.57), economic status (richest aOR = 5.27, 95% CI; 2.85-9.73), parity (seven and above aOR =0.46, 95% CI; 0.34-0.62), wife beating attitude (refuse aOR =1.23, 95% CI; 1.05-1.44) and skilled antenatal care (ANC) (yes aOR = 4.34, 95% CI; 3.10-6.08). CONCLUSION: Uptake of health facility delivery services was seen among women who were educated, exposed to media, wealthy, against wife-beating, attended ANC by skilled attendants and had educated husbands. On the other hand, women from ethnic groups like Poular, those working in agricultural activities, living in rural setting, and those who had more delivery history were less likely to deliver at a health facility. Therefore, there is the need to empower women by encouraging them to use skilled ANC services in order for them to gain the requisite knowledge they need to enhance their utilization of health facility delivery, whiles at the same time, removing socio-economic barriers to access to health facility delivery that occur from low education, poverty and rural dwelling.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".