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Understanding the Challenges of HPV-Based Cervical Screening: Development and Validation of HPV Testing and Self-Sampling Attitudes and Beliefs Scales.

Ovidiu TatarBen HawardPatricia ZhuGabrielle Griffin-MathieuSamara PerezEmily McBrideAisha K LoftersLaurie W SmithMarie-Hélène MayrandEllen M DaleyJulia M L BrothertonGregory D ZimetZeev Rosberger
Published in: Current oncology (Toronto, Ont.) (2023)
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.
Keyphrases
  • high grade
  • mental health
  • cervical cancer screening
  • healthcare
  • quality improvement
  • primary care
  • cross sectional
  • machine learning
  • big data
  • deep learning