Factors influencing the use of supervised delivery services in Garu-Tempane District, Ghana
Bibliographic record
Abstract
BACKGROUND: There is evidence that supervised delivery has the potential to improve birth outcomes for both women and newborns. However, not all women especially in low-income settings like Ghana use supervised delivery services during childbirth. The purpose of this study was to estimate the prevalence of supervised delivery and determine factors that influence use of supervised delivery services in a local district of Ghana. METHODS: A retrospective cross-sectional survey of 322 randomly sampled postpartum women who delivered between January and December 2016 in the Garu-Tempane District was conducted. Structured questionnaires were used to collect data. Descriptive, binary and multivariate logistic regression analysis techniques were used to analyse data. RESULTS: Although antenatal care attendance among respondents was very high 291(90.4%), prevalence of supervised birth was only 219(68%). More than a quarter 103(32%) of the postpartum women delivered their babies at home without skilled birth attendants. After controlling for possible confounders in multivariable logistic regression analyses, factors that strongly independently predicted supervised delivery were religion (p < 0.01), distance to health facility (p < 0.05), making at least 4 antenatal care visits (p < 0.01), national health insurance scheme registration (p < 0.01), satisfaction with services received during antenatal care (p < 0.01), need partner's approval before delivering in health facility (p < 0.01), woman's thoughts that her religious beliefs prohibited health facility delivery(p < 0.01), and woman's belief that there are norms in her community that did not support health facility delivery (p < 0.01). CONCLUSION: There is need for targeted interventions, including community mobilization and health education, and male partner involvement to help generate local demand for, and uptake of, supervised delivery services. Improvement in the quality of services in health facilities, including ensuring respect and dignity for service users, would also be essential.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".