Knowledge, Attitude and Practice of Cervical Cancer Screening through Visual Inspection with Acetic Acid
Bibliographic record
Abstract
Detection of the cervical cancer requires practice of screening that will increase survival rates from the disease. Visual Inspection with Acetic Acid (VIA) is an alternative to screening for cervical cancer.This study aims to assess the knowledge, attitude, and practice towards VIA screening among adult women. We used a cross-sectional study in urban areas of Bangladesh with a sample of 285 respondents those were interviewed through semi-structured questionnaire. Analyses have done by targeting the objectives and considering the indicators with appropriate test statsitsics.About 56.1% respondents aged between 18 and 35 years with majority had at least secondary education including 29.5% were from affluent group. Though the highest majority of women had the knowledge of cervical cancer but only quarter was aware of VIA. A few of the respondents adopted any screening test for detecting cervical cancer and this lower coverage could be due to, among other reasons, lack of knowledge, accessibility, and service availability. The results also indicated that higher educational level of respondents is the predictor of improving knowledge on the disease and early adoption of available test procedure.An awareness building program should be designed for women of early detection of cervical cancer using VIA procedure.
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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 | 0.003 |
| 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 | 0.000 |
| 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".