Pelaksanaan Kelas Ibu Hamil Sebagai Upaya Peningkatan Pengetahuan Ibu, Keluarga Dan Kader Dalam Deteksi Dini Resiko Tinggi Ibu Hamil Di Wilayah Kerja Puskesmas Sambeng Kabupaten Lamongan
Bibliographic record
Abstract
The introduction of high risk pregnant women is done through early detection of risk factors pro actively in all pregnant women by mother, family, and cadres. The program organized by the Ministry of Health to support the step is the Pregnant Women's Class. The purpose of this study is to improve knowledge of mother, family and cadres in early detection of high risk pregnant women in the working area of Sambeng Community Health Center of Lamongan. The design of this research is Quasi Experiment with Pre-Post Design approach. Its population is pregnant mother, family and cadres who attend class of pregnant mother in working area of UPT Sambeng Public Health Center. Sampling technique used was Purposive sampling, got 40 respondents of pregnant women, 40 families and 20 cadres. Data were analyzed with Wilcoxon Signed Ranks Test. Wilcoxon Signed Ranks Test results show that there is influence of maternal class implementation with knowledge of mother (Z value = -4,815), family (Z value = -4,315) and cadre (Z value = -3,162) in early detection of high risk pregnant women. The success of pregnant women's classes can be continued by using innovative and attractive media such as simulation and movie viewing to be more engaging for the community, and families to participate, thus encouraging people to do early detection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".