Pregnancy Examination (Antenatal Care) on the Prevention of High-Risk Pregnancy Events Accelerated the Referral System Efforts to Avoid Maternal Death in Dairi Regency North Sumatra, Indonesia
Bibliographic record
Abstract
Introduction: The pregnancy screening websites evaluated in this study were accessed through various hardware devices, including personal computers (PCs), laptops, notebooks, and smartphones. Among various hardware options, researchers have chosen smartphones as a suitable container due to their widespread use, portability, space efficiency, effectiveness, efficiency, affordability, and practicality. Smartphones for some people, are phones that work using all operating system software that provides standard and fundamental relationships for application developers. In addition, smartphones are designed to work through an operating system, which allows users to freely add applications, functions, or make changes as desired, much like a computer. This includes the ability to operate a website. Methods: This study was a case-control study. The sample included 60 pregnant women, with 30 having high-risk pregnancies and 30 having normal pregnancies. The analyses used are univariate and bivariate. Bivariate analysis employs the chi-square statistical test with a confidence level of 95%. Multivariate analysis using multiple logistic regression. Results: The results indicate that knowledge, attitudes, actions, family income, support from family and posyandu cadres have an impact on the prevention of high-risk pregnancy events. Among these factors, family support was found to be the most influential. However, the study also revealed that efforts to prevent high-risk pregnancy events through pregnancy examination (antenatal care) have not been fully optimized. Conclusion: The study concludes that arranging the order of pregnancies can prevent high-risk pregnancies in Dairi Regency. It is recommended that the government and related parties improve the implementation and supervision of pregnancy care to prevent high-risk pregnancies. This will ensure that the referral system operates effectively, ultimately reducing the incidence of maternal death.
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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 | 0.000 |
| 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".