What Factors Are Associated with Attitudes towards HPV Vaccination among Kazakhstani Women? Exploratory Analysis of Cross-Sectional Survey Data
Bibliographic record
Abstract
Background. The high prevalence of HPV infection among Kazakhstani women and the absence of an HPV vaccination program are directly reflected in increasing rates of cervical cancer incidence and mortality. Kazakhstan made its first attempt at introducing the HPV vaccine in 2013, but was unsuccessful due to complications and low public acceptance. The attitudes of Kazakhstani women towards the vaccine were never measured. Therefore, this study aims to investigate the attitudes of women towards the HPV vaccine and determine factors associated with positive, negative, or neutral attitudes. Methods. A 29-item survey consisting of 21 demographic and contextual questions and 8 Likert-scale questions was distributed among women attending gynecological offices in four major cities of Kazakhstan from December 2021 until February 2022. Attitudes of women were measured based on their answers to the eight Likert-scale questions. Ordinal logistic regression was built to find associations between demographic characteristics and attitudes of women. Results. Two hundred thirty-three women were included in the final analysis. A total of 54% of women had positive attitudes towards the vaccine. The majority of women did not trust or had a neutral attitude towards the government, pharmaceutical industry, and traditional and alternative media. However, the trust of women was high in medical workers and scientific researchers. Women’s age, education, number of children, effect of the 2013 HPV program, and trust in alternative medicine were included in the ordinal logistic model. Women with a low level of education, a high number of children, who believe in alternative medicine, and who were affected by the failed 2013 vaccination program were less likely to have a positive attitude towards the vaccine. Conclusions. Contrary attitudes towards HPV vaccination exist among Kazakhstani women, with approximately half having positive and almost half having negative or neutral attitudes towards the vaccine. An informational campaign that takes into consideration women’s levels of trust in different agencies, as well as targets those who are the most uninformed, might help in a successful relaunch of the HPV vaccination program. However, more studies that cover a higher number of women are required.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".