Knowledge about human papillomavirus and prevention of cervical cancer among women of Arkhangelsk, Northwest Russia
Bibliographic record
Abstract
BACKGROUND: Knowledge about cervical cancer (CC) risk factors and benefits of CC prevention motivates women to participate in its screening. However, several studies show that there is a significant knowledge deficit worldwide about human papillomavirus (HPV). The current study explores the level of knowledge about HPV and CC prevention in the context of sociodemographic and behavioral characteristics of women who visited an antenatal clinic in Arkhangelsk, Russia. METHODS: This cross-sectional study was conducted in the city of Arkhangelsk, which seats the administrative center of Arkhangelsk County, Northwest Russia. It included women who consulted a gynecologist for any reason between January 1, 2015 and April 30, 2015, were residents of Arkhangelsk, 25 to 65 years of age and sexually active (N = 300). Student's t-test for continuous variables and Pearson's χ2 test for categorical variables were used in the comparisons of women grouped as having either poor or sufficient knowledge. Linear regression analysis was also employed. RESULTS: The level of knowledge about HPV and CC prevention was associated with education, parity, age of initiating of intercourse, and sources of information. After adjustment, women with university education were more likely to have higher knowledge about HPV and CC prevention compared to those with lower education. CONCLUSIONS: We observed that most participants had a sufficient level of knowledge. Educational gaps were identified that potentially could be used to tailor interventions in CC prevention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".