Increasing institutional deliveries among antenatal clients: effect of birth preparedness counselling
Bibliographic record
Abstract
The World Health Organization recommends birth and emergency preparedness (BEP) as essential components of the Focused Antenatal Care model. The purpose of providing BEP messages to women during their antenatal visits is to increase the use of skilled attendance at childbirth. However, the effectiveness of this component has not yet been clearly established in routine contexts. This retrospective cohort study examined the association between exposing women to BEP messages during antenatal visits and the use of the skilled attendance at childbirth in two rural districts of Burkina Faso (Koupela and Dori). The study included 456 antenatal care users in 30 rural health centres in these two districts. Data were collected using modified questionnaires from the Johns Hopkins Program for International Education in Gynecology and Obstetrics and from demographic and health surveys. Logistic regression was performed with a model of generalized estimating equation to adjust for clustered effects. In the Koupela district, where the rate of institutional deliveries (80%) was relatively high, the use of BEP messages was not associated with an increase in institutional deliveries. In contrast, in the district of Dori, where the rate of institutional deliveries (47%) was lower, messages regarding danger signs [Adjusted Odds Ratio (AOR) = 1.93; 95% Confidence Interval (CI): 1.07, 3.49] and cost of care (AOR = 2.13; 95% CI: 1.09, 4.22) were associated with an increased probability of institutional births. Based on these results, it appears that birth and emergency preparedness messages provided during antenatal visits may increase the use of skilled attendance (increase the rate of institutional births) in areas where institutional births are low. Therefore, it is important to adapt the content of the messages to meet the particular needs of the users in each locality. Furthermore, BEP counselling should be implemented in health facilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".