Dissecting antenatal care inequalities in western Nepal: insights from a community-based cohort study
Bibliographic record
Abstract
Abstract Background Antenatal care (ANC) ensures continuity of care in maternal and foetal health. Understanding the quality and timing of antenatal care (ANC) is important to further progress maternal health in Nepal. This study aimed to investigate the proportion of and factors associated with, key ANC services in western Nepal. Methods Data from a community-based cohort study were utilized to evaluate the major ANC service outcomes: (i) three or less ANC visits (underutilization) (ii) late initiation (≥ 4 months) and (iii) suboptimal ANC (< 8 quality indicators). Mothers were recruited and interviewed within 30 days of childbirth. The outcomes and the factors associated with them were reported using frequency distribution and multiple logistic regressions, respectively. Results Only 7.5% of 735 mothers reported not attending any ANC visits. While only a quarter (23.77%) of mothers reported under-utilizing ANC, more than half of the women (55.21%) initiated ANC visits late, and one-third (33.8%) received suboptimal ANC quality. A total of seven factors were associated with the suboptimal ANC. Mothers with lower education attainment, residing in rural areas, and those who received service at home, were more likely to attain three or less ANC visits, late initiation of ANC, and report receiving suboptimal ANC. Furthermore, mothers from poor family backgrounds appeared to initiate ANC late. Mothers from disadvantaged Madhesi communities tended to receive suboptimal ANC. Conclusions Despite a high ANC attendance, a significant proportion of mothers had initiated ANC late and received suboptimal care. There is a need to tailor ANC services to better support women from Madhesi ethnic community, as well as those with poor and less educated backgrounds to reduce the inequalities in maternal health care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".