Sociodemographic Determinants of Reproductive Healthcare Service Use Among Pregnant Women in Pakistan
Bibliographic record
Abstract
INTRODUCTION: Using the essential reproductive care services such as antenatal care (ANC) and skilled birth services are vital for ensuring safe motherhood and controlling maternal and child mortality. There is no recent evidence on the state of using reproductive care services in Pakistan women. The purpose of the cross-sectional study is to explore the timing and frequency of ANC, the hospital and other institutional delivery, the cesarean section (C-section) services and to identify the sociodemographic factors that are associated with the use of these services. METHODOLOGY: Using the latest Pakistan Demographic and Health Survey (2017-18 PDHS) for this analysis, the data were collected by face-to-face interviews by trained interviewers, which included 8287 women aged 15-49 years. The data on reproductive services were defined by standard guidelines by the World Health Organization (WHO). Data analyses involved univariate tests and multivariate regression techniques. RESULTS: The percentage of women who attended ANC visits in the first trimester was 62.59%, and those who attended the minimum recommended number of four visits was 49.46%. The percentages of using hospital and C-section services were, respectively, 76.20% and 19.63%. In the regression analysis, place of residence, education, household wealth status, access to using electronic media and learning about family planning from electronic media and before marriage were found to significantly predict the use of ANC and facility delivery services. However, educational and household wealth status stood out as the strongest predictors of all. About half of the women were not having adequate ANC visits and about one-third not making timely ANC contact. More than three-quarters reported choosing to deliver at hospital/other facility, and about one-fifth preferred C-section. CONCLUSIONS: The results indicated that, among the predictor of using these services, education and household wealth status were found to have the strongest association, highlighting the role of women's socioeconomic well-being in availing the basic reproductive healthcare services. Hence, this study suggests that the government and medical institutions should further pay attention to the ANC visits and reduce infant birth mortality rates. Simultaneously, increasing women's educational opportunities, enhancing women's socioeconomic well-being and social status, can help improve their health awareness and promote healthy behaviors.
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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 | 0.001 | 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".