Antenatal care and skilled birth in the fragile and conflict‐affected situation of Burundi
Bibliographic record
Abstract
Burundi is a fragile and conflict-affected state characterized by persistent conflict and political violence. Amid this conflict, Burundi has one of the highest maternal mortality rates globally-548 per 100,000 births as of 2017, such deaths could be prevented with antenatal care (ANC). This cross-sectional study aimed to examine the association between conflict and ANC and skilled birth attendant (SBA) utilization. Logistic regression analysis was conducted using the 2016-2017 Burundi Demographic and Health Survey (n = 8581), as well as a Near Analysis Geographic Information System exploration. Results show that women in extremely high conflict regions were less likely to have four antenatal visits (odds ratio [OR] = 0.79, p < 0.05). However, they were more likely to use a SBA (OR = 2.31, p < 0.001) and to deliver in a hospital (OR = 1.69, p < 0.001). As well, gender equality, education, and watching television were correlated with an increased likelihood of utilization. In contrast, unwanted pregnancies and increased parity were correlated with decreased use. Moreover, with renewed violence erupting in 2015, uptake of care has likely further stagnated or declined. If Sustainable Development Goal 3.1's objective of reducing maternal mortality globally is to be achieved, women's access to maternal healthcare services in conflicted-affected areas such as Burundi must be improved.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".