Factors Associated with Utilization of Antenatal Care Services among Women in Noakhali District, Bangladesh
Bibliographic record
Abstract
A cross-sectional study sought to uncover the determinants affecting antenatal care (ANC) utilization among pregnant women in Noakhali District, Bangladesh. Data from 400 women were gathered utilizing a standardized questionnaire, and the analysis was performed using SPSS software (version 23.0), incorporating descriptive statistics, Chi-square tests, and multinomial logistic regression. The research revealed that merely 47.8% of individuals attended a minimum of four antenatal care visits, as advised by healthcare recommendations. The frequency of ANC visits was substantially correlated with socioeconomic status, educational attainment, work status, and obstacles to receiving ANC services. Mothers with 1-3 antenatal care visits encountered 26.58 times more barriers to attending visits than those who completed four or more visits (OR: 26.58, 95% CI: 11.28-62.62). Moreover, income levels were 0.53 times greater among mothers with fewer antenatal care appointments in comparison to those with four visits (OR: 0.53, 95% CI: 0.32-0.86). The report emphasizes the need to address the constraints preventing women from getting enough ANC. This necessitates enhancements in transportation, the quality of healthcare facilities, and the training and accessibility of healthcare staff. Enhancing awareness of the significance of ANC through focused campaigns is essential at the local, national, and worldwide levels. Addressing these challenges is crucial for enhancing ANC utilization and promising improved maternal and newborn health outcomes in the region.
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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) | 0.001 | 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".