Sociological study of awareness and involvement of women in Vinnytsia in breast cancer screening
Bibliographic record
Abstract
Annotation. Almost 30% of breast cancer (BC) cases in Ukraine are registered in advanced stages, which requires improvement of secondary prevention of BC. The aim of the study was to assess the knowledge of the female population of Vinnytsia about the methods of timely diagnosis of BC and their adherence to BC screening. The sociological study was carried out according to a specially developed program through an anonymous online survey on the Google Forms platform. The link to the questionnaire was sent out in public chats on social networks in Vinnytsia in October 2024. The study covered 403 Vinnytsia women over 18 years of age. The database was formed, and its statistical analysis was carried out using Microsoft Excel. The statistical significance of differences between the comparison groups was assessed using the Pearson χ2 criterion. The results of the study showed low awareness and medical activity of women regarding secondary prevention of BC. Almost a quarter (22.4±2.1%) of the respondents had never performed Breast Self-Examination (BSE). The main reasons for the lack of practice of BSE indicated by women were lack of time (38.0±2.4%), lack of knowledge and skills in the method of BSE (30.0±2.3%). Among women practicing BSE, only 30.1±2.3% do it with the required frequency - once a month. Women realize the importance of screening for BC using mammography and breast ultrasound (82.7±1.9% of respondents indicated the need for annual examinations), but in practice, 58.3±2.5% of them have never had a mammogram in their lives, and 32.0±2.3% – breast ultrasound. The lowest levels of awareness and adherence to BC screening were found among young women (18-34 years old) and single women. Women health workers are more involved in breast cancer screening than representatives of other professions, but even among them, the proportion of women who do not know methodic of BSE was 15.8±2.3%, have never had a breast ultrasound in their life – 30.0±2.9%, mammography – 55.2±3. Information and explanatory work among women need to be improved to increase their awareness of secondary breast cancer prevention methods, the importance of screening tests for early detection of breast cancer and the formation of a responsible attitude to their health.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".