Factors affecting the utilization of antenatal care services among women in Guinea: a population-based study
Bibliographic record
Abstract
INTRODUCTION: Evidence suggests that a better understanding of determinants of antenatal care (ANC) utilization is crucial to reducing maternal and child deaths. Little is known about the utilization of ANC services in Guinea. OBJECTIVE: The aim of this study was to explore factors determining utilization of skilled ANC in Guinea. METHODS: This study focused on a sample of 7812 ever married women. Using multivariate logistic regression, factors associated with the utilization of ANC were identified. The output of the multivariate logistic regression was presented using adjusted odds ratio and the corresponding 95% confidence interval (CI). RESULTS: Several factors had significant association with utilization of skilled ANC service in Guinea: having decision-making power (2.21, 95% CI: 1.63, 3.00), employment status (1.86, 95% CI: 1.39, 2.48), media exposure (1.60, 95% CI: 1.26, 2.02), maternal education (2.68, 95% CI: 1.36, 5.28), husband/partner education status (1.66, 95% CI: 1.08, 2.55), household economic status (2.19, 95% CI: 1.40, 3.44), place of residence (0.29, 95% CI: 0.16, 0.50) and ethnicity (0.40, 95% CI: 0.23, 0.69). CONCLUSIONS: These findings suggest a variety of socio-demographic and economic factors as well as media exposure are associated with women's use of ANC. Policymakers should implement appropriate measures to address the existing variations and gaps in ANC services utilization among different subgroups of women in Guinea.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".