Understanding the Challenges of HPV-Based Cervical Screening: Development and Validation of HPV Testing and Self-Sampling Attitudes and Beliefs Scales
Bibliographic record
Abstract
The disrupted introduction of the HPV-based cervical screening program in several jurisdictions has demonstrated that the attitudes and beliefs of screening-eligible persons are critically implicated in the success of program implementation (including the use of self-sampling). As no up-to-date and validated measures exist measuring attitudes and beliefs towards HPV testing and self-sampling, this study aimed to develop and validate two scales measuring these factors. In October-November 2021, cervical screening-eligible Canadians participated in a web-based survey. In total, 44 items related to HPV testing and 13 items related to HPV self-sampling attitudes and beliefs were included in the survey. For both scales, the optimal number of factors was identified using Exploratory Factor Analysis (EFA) and parallel analysis. Item Response Theory (IRT) was applied within each factor to select items. Confirmatory Factor Analysis (CFA) was used to assess model fit. After data cleaning, 1027 responses were analyzed. The HPV Testing Attitudes and Beliefs Scale (HTABS) had four factors, and twenty-two items were retained after item reduction. The HPV Self-sampling Attitudes and Beliefs Scale (HSABS) had two factors and seven items were retained. CFA showed a good model fit for both final scales. The developed scales will be a valuable resource to examine attitudes and beliefs in anticipation of, and to evaluate, HPV test-based cervical screening.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".