The role of community-based health services in influencing postnatal care visits in the Builsa and the West Mamprusi districts in rural Ghana
Bibliographic record
Abstract
BACKGROUND: Globally, maternal mortality is still a challenge. In Ghana, maternal morbidity and mortality rates remain high, particularly in rural areas. Postnatal Care (PNC) is one of the key strategies for improving maternal health. This study examined determinants of at least three PNC visits in rural Ghana. METHODS: We conducted a cross-sectional study at the Community-Based Health Planning and Services (CHPS) Zones in the Builsa and West Mamprusi Districts between April and June 2016. We selected 650 women who delivered within 5 years preceding the survey (325 from each of the two sites) using the two-stage random sampling technique. RESULTS: Of the 650 respondents, 62% reported attending postnatal care at least three times. In the Builsa district, the percentage of women who made at least three PNC visits were 90% compared with 35% in the West Mamprusi district. Older women and those who attended antenatal clinics at least four times (AOR: 5.23; 95% CI: 2.49-11.0) and women who had partners with some secondary education (AOR: 3.31; 95% CI: 1.17-9.39) were associated with at least three PNC visits. CONCLUSIONS: Men engagement in maternal health services and the introduction of home-based PNC services in rural communities could help health workers reach out to many mothers and children promptly and improve PNC visits in those communities.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".