The Influence of Education-Information-Communication (EIC) Based on Karo Culture for the Early Detection of Cervical Cancer at the Karo Region
Bibliographic record
Abstract
Education, Information and Communication (EIC) based on Karo Culture on Knowledge and Attitude of Women of Childbearing Age for the Early Detection of Cervical Cancer at the Village of Cinta Rakyat, Merdeka District, Karo Region. Quasi experiment with non-equivalent pre-test and post-test, using a control group with the intervention of EIC based on Karo culture through counseling in Cinta Rakyat village, Merdeka district and Karo Regency. In order to know the influence of EIC based on Karo culture to knowledge and attitude, this research conducted an independent t-test with normality-test as starting point. This research also employed pair t-test to know two different variables: knowledge and attitude. Purposive sampling was conducted with univariate and bivariate data analysis.There is an influence of EIC based on Karo culture through composed songs and traditional dance called Landek on knowledge and attitude before and after the intervention (p = 0,000). This research found that there is an influence of counseling on knowledge and attitude before and after the intervention (p = 0,000). There is no difference between EIC based on culture through composed songs and dance, and counseling to increase knowledge (p = 0,498). However, EIC based on culture is more effective way than counseling to develop attitude toward cervical cancer prevention (p = 0,027). This research, therefore, argues that health officers/Promkes at the Health Office and at the center of community health/PUSKESMAS are expected to conduct counseling on cervical cancer using composed songs and dance (Landek) for prevention strategy, especially in the area of the center of community health in Karo District.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".