Towards reduction of maternal and perinatal mortality in rural Burkina Faso: communities are not empty vessels
Bibliographic record
Abstract
BACKGROUND: Reducing maternal and perinatal mortality in sub Saharan Africa remains challenging and requires effective and context specific interventions. OBJECTIVE: The aims of this paper were to demonstrate the impact of the community mobilisation of the Skilled Care Initiative (SCI) in reducing maternal and perinatal mortality and to describe the concept and implementation in order to guide replication and scaling up. DESIGNS: A quasi experimental design was used to assess the extent to which the SCI was associated with increased institutional births, maternal and perinatal mortality reduction in an intervention (Ouargaye) versus a comparison (Diapaga) district. A geo-referenced census was conducted to retrospectively assess changes in outcomes and process measures. A detailed description of activities, rationale and timing of implementation were gathered from the SCI project officers and summarised. Data analyses included descriptive statistics and multivariate analyses. RESULTS: At macro level, the main significant difference between Ouargaye and Diapaga districts was the scope and intensity of the community-based interventions implemented in Ouargaye. There was a temporal association relationship before and after the implementation of the demand-driven interventions and a remarkable 30% increase in institutional births in the intervention district compared to 10% increase in comparison district. There was a significant reduction of perinatal mortality rates (OR =0.75, CI 0.70-0.80) in intervention district and a larger decrease in maternal mortality ratios in intervention district, although statistical significance was not reached. A comprehensive framework of community mobilisation strategy is proposed to improve maternal and child health in poorest communities. CONCLUSION: Controlling for the availability and quality of health services, working in partnership and effectively with communities, and not for them - hence characterising communities as not being empty vessels - can have impacts on outcomes. Here, in the district with a community mobilisation programme, there was a marked increase in institutional births and reductions in maternal and perinatal deaths.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".