Determinants of Antenatal Morbidity: A Multivariate Analysis
Bibliographic record
Abstract
OBJECTIVES: The aim of this paper was to investigate the potential risk factors for developing complications and their magnitude during the antenatal period. METHODOLOGY: The data used in this paper came from a prospective survey in rural areas of Bangladesh conducted by the Bangladesh Institute of Research for Promotion of Essential and Reproductive Health and Technologies (BIRPERHT) between November 1992 and December 1993. The differential patterns were analyzed for respondents' selected characteristics, and multivariate analysis was performed employing logistic regression and proportional hazards models for life-threatening and high-risk complications during pregnancy. RESULTS: For life-threatening complications during pregnancy, several factors emerged as potential risk factors, such as number of the pregnancy, age at marriage, duration of pregnancy, economic status and history of anemia prior to the index pregnancy. The last two covariates were associated only in the proportional hazards. Potential risk factors for high-risk complications during pregnancy were level of education, age at marriage, wanted pregnancy, duration of pregnancy and economic status. CONCLUSIONS: Health planners and policy makers in developing countries are trying to facilitate health services at the doorsteps of rural people. Our findings will help them understand the magnitude and underlying determinants of maternal morbidities and help their health planning process to reduce both life-threatening and high-risk complications during the antenatal period. Early age at marriage needs to be prevented through encouragement of girls' education as well as through increased social awareness programs. An effective quick referral mechanism should be developed to provide emergency services to high risk-groups. Finally, the importance of additional food supplements needs to be promoted during antenatal care visits as well as through mass media in order to reach people living in remote areas of rural Bangladesh.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".