Factors influencing the use of antenatal care in rural West Sumatra, Indonesia
Bibliographic record
Abstract
BACKGROUND: Every year, nearly half a million women and girls needlessly die as a result of complications during pregnancy, childbirth or the 6 weeks following delivery. Almost all (99%) of these deaths occur in developing countries. The study aim was to describe the factors related to low visits for antenatal care (ANC) services among pregnant women in Indonesia. METHOD: A total of 145 of 200 married women of reproductive age who were pregnant or had experienced birth responded to the questionnaire about their ANC visits. We developed a questionnaire containing 35 items and four sections. Section one and two included the women's socio demographics, section three about basic knowledge of pregnancy and section four contained two subsections about preferences about midwives and preferences about Traditional Birth Attendant (TBA) and the second subsections were traditional beliefs. Data were collected using a convenience sampling strategy during July and August 2010, from 10 villages in the Tanjung Emas. Multiple regression analysis was used for preference for types of providers. RESULTS: Three-quarter of respondents (77.9%) received ANC more than four times. The other 22.1% received ANC less than four times. 59.4% received ANC visits during pregnancy, which was statistically significant compared to multiparous (p = 0.001). Women who were encouraged by their family to receive ANC had statistically significant higher traditional belief scores compared to those who encouraged themselves (p = 0.003). Preference for TBAs was most strongly affected by traditional beliefs (p < 0.001). On the contrary, preference for midwives was negatively correlated with traditional beliefs (p < 0.001). CONCLUSIONS: Parity was the factor influencing women's receiving less than the recommended four ANC visits during pregnancy. Women who were encouraged by their family to get ANC services had higher traditional beliefs score than women who encouraged themselves. Moreover, traditional beliefs followed by lower income families had the greater influence over preferring TBAs, with the opposite trend for preferring midwives. Increased attention needs to be given to the women; it also very important for exploring women's perceptions about health services that they received.
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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 | 0.001 |
| 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 | 0.001 | 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".