Awareness and Knowledge about Human Papillomavirus Infection and Vaccination among Women in UAE
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is the second commonest female cancer worldwide. The 50-55 cases of cervical cancer are reported annually in the UAE. There is a scarcity of data from Middle Eastern region regarding knowledge and attitude of women towards HPV infection, cervical cancer prevention and HPV vaccine. The aim of our study was to assess the knowledge of women regarding HPV infection and vaccine in UAE. MATERIALS AND METHODS: A cross-sectional survey of 640 women aged 18-50 years was conducted in Al-Ain district in UAE using convenience sampling. Women with previous diagnosis of cervical cancer, non- residents of UAE, younger than 18 or older than 50 years of age and those unable to speak Arabic or English were excluded from the study. Logistic regression analysis was performed to assess the association of HPV knowledge with independent factors like age, education etc. RESULTS: Only 29% of our sampled women have ever heard of HPV infection. Only 15.3% women recognized it as STI. Only about 22% women have also heard of the HPV vaccine. Three quarter of the women in our study thought that cervical cancer can be prevented. About 28% recognized vaccine as a preventive measure against cervical cancer. Age (AOR 1.049, 95%CI 1.02-1.08) and husband's level of education were found to be significant (p value 0.015) after adjusting for women's age. CONCLUSIONS: The knowledge of HPV infection and vaccine is low in the UAE. Few women recognized HPV as sexually transmitted infection. Increasing age and husband's education are associated with better knowledge of HPV infection.
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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 | 0.003 |
| 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 | 0.000 |
| 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".