EXPLAINING THE GAP IN ANTENATAL CARE SERVICE UTILIZATION BETWEEN YOUNGER AND OLDER MOTHERS IN GHANA
Bibliographic record
Abstract
Over two-thirds of pregnant women (69%) have at least one antenatal care (ANC) coverage contact in sub-Saharan Africa. However, to achieve the full life-saving potential that ANC promises for women and babies, a nuanced understanding of age-specific gaps in utilization of ANC services is required. Using the 2008 Ghana Demographic and Health Survey of 1456 individuals, this study examined the disparities in the use of ANC services between younger and older mothers by applying four counterfactual decomposition techniques. The results show that cross-group differences in the explanatory variables largely account for the differentials in ANC service utilization between younger and older mothers. Birth order (parity) accounts for the largest share of the contribution to the overall explained gap in ANC utilization between the younger and older mothers, suggesting that ANC differentials between the two groups are probably due to biosocial factors. To a lesser extent, wealth status of the two groups also contributes to the overall explained gap in ANC service utilization. The policy implications of these findings are that in order to bridge the ANC service utilization gap between the two groups, policymakers must systematically address gaps in cross-group differences in the explanatory variables in order to increase the utilization of ANC to attain the minimum recommendation of four visits as per World Health Organization guidelines.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".