Effects of a community-based intervention package on postnatal care seeking behavior in rural Bangladesh: a cluster-randomized controlled trial
Bibliographic record
Abstract
Background: Community-based Postnatal Care (PNC) initiatives have been found to improve maternal and neonatal health. Objectives: This paper aims at evaluating the effectiveness of a Community-Based Intervention Package in providing ‘limited’ PNC services by Community Support Systems (CmSS) and in increasing maternal PNC visits from Skilled Healthcare Providers (SHPs) in rural Bangladesh as well as identifying the predictors of maternal PNC from SHPs. Methods: A cluster-randomized controlled trial was employed where 6 clusters (each with an average population of about 28,000) of Narsingdi District were randomly assigned to the intervention and the comparison group. Sample sizes for pre- and post-intervention were 675 and 702, respectively, collected in June 2010 and December 2011, respectively, from mothers with a recent live birth. Logistic regression was used in examining the main outcomes and the predictors of maternal PNC from SHPs. Results: The coverage of ‘limited’ PNC services by the CmSS members to the mothers did not increase significantly (p=0.25), nor did the maternal PNC from SHPs (p=0.11). Both delivery at a Healthcare Facility and delivery by SHPs increased the odds of taking at least one PNC from SHPs 10-fold with 95% confidence intervals of 4.52-24.04 (p Conclusion: This intervention was found to be effective neither in providing limited PNC services by the CmSS members, nor in increasing maternal PNC from SHPs in our study. Further research with proper monitoring and sufficient number of clusters is recommended.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".