High risk fertility behaviour and health facility delivery in West Africa
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that women who give birth in a health facility have lower odds of experiencing pregnancy complications and significantly reduced risk of death from pregnancy-related causes compared to women who deliver at home. Establishing the association between high-risk fertility behaviour (HRFB) and health facility delivery is imperative to inform intervention to help reduce maternal mortality. This study examined the association between HRFB and health facility delivery in West Africa. METHODS: Data for the study were extracted from the most recent Demographic and Health Surveys of twelve countries in West Africa conducted from 2010 to 2020. A total of 69,479 women of reproductive age (15-49 years) were included in the study. Place of delivery was the outcome variable in this study. Three parameters were used as indicators of HRFB based on previous studies. These were age at first birth, short birth interval, and high parity. Multivariable binary logistic regression analysis was performed to examine the association between HRFB and place of delivery and the results were presented using crude odds ratio (cOR) and adjusted odds ratio (aOR), with their respective 95% confidence interval (CI). RESULTS: More than half (67.64%) of the women delivered in a health facility. Women who had their first birth after 34 years (aOR = 0.52; 95% CI = 0.46-0.59), those with short birth interval (aOR = 0.91; 95% CI = 0.87-0.96), and those with high parity (aOR = 0.58; 95% CI = 0.55-0.60) were less likely to deliver in a health compared to those whose age at first delivery was 18-34 years, those without short birth interval, and those with no history of high parity, respectively. The odds of health facility delivery was higher among women whose first birth occurred at an age less than 18 years compared to those whose age at first birth was 18-34 years (aOR = 1.17; 95% CI = 1.07-1.28). CONCLUSION: HRFB significantly predicts women's likelihood of delivering in a health facility in West Africa. Older age at first birth, shorter birth interval, and high parity lowered women's likelihood of delivering in a health facility. To promote health facility delivery among women in West Africa, it is imperative for policies and interventions on health facility delivery to target at risk sub-populations (i.e., multiparous women, those with shorter birth intervals and women whose first birth occurs at older maternal age). Contraceptive use and awareness creation on the importance of birth spacing should be encouraged among women of reproductive age in West Africa.
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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 | 0.001 | 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) | 0.001 | 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".