PENGARUH PENDIDIKAN KESEHATAN TERHADAP PENGETAHUAN DAN SIKAP WANITA USIA SUBUR TENTANG PEMERIKSAAN IVA TEST
Bibliographic record
Abstract
IVA Test is a simple method in early detection of cervical cancer early. The target of IVA Test is WUS aged 15-49 years. In Bengkulu Province WUS who perform the examination of cervical cancer detection by IVA test method is still low. The purpose of this study is to determine the effect of health education on knowledge and attitude of WUS on examination of IVA test in the work area of Sukamerindu Puskesmas Bengkulu City 2017 ".This research use pre experiment method one group pretest - posttest design. Population taken in this research is married WUS aged 20-49 years in work area of Sukamerindu health center with sample amounted to 30 people taken by purposive sampling technique. Sampling is done by purposive sampling technique. Data analysis using T-dependent Test.The results of this study obtained the average knowledge before the provision of health education that is 6.80 and the average after 13.00 with mean difference 6.2.And obtained the average attitude before the provision of health education that is 35.50 and the average after 38.80 with mean difference 3,3. It was concluded that health education influenced WUS knowledge and attitude about IVA test in Sukamerindu Puskesmas area of Bengkulu city in 2017.It is expected that the puskesmas can actively conduct home visits to provide health education, especially in providing information about the examination of IVA test to married WUS to reduce the risk of cervical cancer.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".