The Analysis of Factor that Associated the Antenatal Care (ANC) Visit in Pregnant Woman during the COVID-19 Pandemic at Blooto Health Center, Mojokerto
Bibliographic record
Abstract
Background: The COVID-19 pandemic situation has harmed various sectors. Maternal and child health services are also affected in terms of access and quality. Based on the health profile of Mojokerto City in 2021, Blooto Health Center has the highest K1 (100.7%) and K4 (98.1%) coverage in 2020 compared to other health centers in Mojokerto City. In 2021 in the first quarter of K1 coverage decreased compared to 2020. In January 2020 and 2021, it was 10% and 8.63%, February 2020 and 2021 were 19% and 17.79%, and March 2020 and 2021 were 29, 4%, and 28.3%. Objectives: This study aims to analyze what factors are associated with Antenatal Care (ANC) visits to pregnant women during the COVID-19 pandemic in the work area of the Blooto Health Center, Mojokerto City. Methods: This study used a cross-sectional research design. The research population was 182 pregnant women with a sample of 79 pregnant women. The sampling technique is simple random sampling. The independent variables of the study were education level, employment status, disease history, age, parity, knowledge, attitude, application of health protocols, distance from residence to health facilities, the role of medical personnel, and availability of health protocol facilities at health care facilities. The dependent variable of the study was antenatal visits. Primary data collection using online questionnaires and secondary data, namely the maternal cohort book. Data analysis used univariable and bivariable analysis by calculating the Prevalence Ratio (PR) value. Results: There is a relationship between education level, knowledge, attitude, application of health protocols, the distance of residence, role of medical personnel, and availability of health protocol facilities with ANC visits during the COVID-19 pandemic. Meanwhile, age, employment status, disease history, and parity were not related to ANC visits during the COVID-19 pandemic. Conclusions: Education level, knowledge, attitude, application of health protocols, the distance of residence, role of medical personnel, and availability of health protocol facilities related to ANC visits during the COVID-19 pandemic. Health workers and community leaders are expected to provide education to pregnant women on the importance of doing ANC checks and apply health protocols during the pandemic.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".