Edukasi Pemeriksaan Inspeksi Visual Asam Asetat (IVA) pada Wanita Usia Subur (WUS) di Wilayah Kerja Puskesmas Tanah Luas Kabupaten Aceh Utara
Bibliographic record
Abstract
Cervical cancer is the fourth most common cancer affecting women worldwide. Prevention and screening are by far the most effective modalities for reducing health care burden and mortality from cervical cancer. In Indonesia, cervical cancer is the second largest cancer disease after breast cancer. Many women are still unable to detect cervical cancer at an early stage for various reasons. Personal, social, cultural and structural factors are the most important barriers to cervical cancer screening. The problem-solving method used is education through presentations about IVA examinations by distributing leaflets and posters and carrying out IVA examinations. When carrying out community service activities, there were 30 women of childbearing age who attended. Community service activities carried out for two days in the Tanah Luas Community Health Center Work Area, North Aceh Regency, there was an increase in the knowledge of the community service target audience regarding IVA Examination education, the pretest evaluation of targets who had good knowledge was 40% and in the posttest evaluation good knowledge was 90%. The entire target audience (30 WUS) actively participated in the IVA examination. It is important for health workers to continue to provide information and education about the importance of VIA examinations as an effort to detect cervical cancer early.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".