Effect of Training on the Application of ANC 10 T on Midwife Knowledge to Prevent Maternal Death in Dairi Regency of North Sumatra Indonesia
Bibliographic record
Abstract
INTRODUCTION: Antenatal Care (ANC) data collected at Matlab Thana and shows that the maternal mortality rate for mothers aged 15 – 19 years is 7.4 per 1000 live births while for mothers aged 20 – 24 it is 3.8 to 1000 live births. For mothers under the age of 15, 17, 7. This ratio was then cited in the 1989 WHO volume compilation of age-specific maternal mortality data from 40 countries over approximately the same period as the Bangladesh study suggesting a much more moderate risk of excess for young mothers, but most of them were ignored in the scientific literature. Rizki Dewi et al’s 2017 research-led most midwives (69.7%) 10T standard in good integrated antenatal service. The purpose of the research to know the knowledge of midwives in the application of antenatal care to prevent maternal death in Dairi Regency of North Sumatra. METHODS: Design research was quantitative research with a quantitative design experiment. Models used, observed/measured changes. Bivariate Analysis, to analyze the influence between independent variables (each dimension) and dependent variables with different tests (t-tests). RESEARCH RESULTS: Midwife knowledge increased after ANC implementation training. Knowledge Before an ANC 10 T application training, value p = 0.731 This means that there was no difference in knowledge, control before (pre-test) training implementation of ANC 10 T. The average knowledge difference between the pre-test and post-test is 0.067 with a value of p = 0.058 The difference between the case group and the control group before and after the training of an ANC 10 T implementation with a value of p = 0.000, p-value (0.000) < 0.05 then it can be concluded that the training of the implementation of ANC 10 T effectively improves midwife knowledge in Dairi Regency of Sumatra Province. CONCLUSION: Midwife knowledge increased after an ANC implementation training in the Dairi regency of North Sumatra. Advice that training was very effective to improve the knowledge of midwives in the application of ANC in Dairi district of North Sumatra.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".