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  • br Women were asked to

    2020-08-18


    Women were asked to click on the URL imbedded in the online study advertisement if they PEG300 wished to participate in the study. After reading the information statement and indicating their consent to participate, they were asked to list the cervical cancer screening bar-riers and facilitators they had experienced. They were then provided with a short debriefing sheet that explained cervical cancer screening and included online sources of screening information.
    3. Results
    There were no missing data. Several univariate outliers were de-tected on the continuous variables (number of screening barriers & facilitators), but they were retained as they represented clinically sig-nificant responses. No differences were found between the 5% trimmed means and the untrimmed variable means. Continuous variables were all positively skewed. Shapiro-Wilk and Kolmogorov-Smirnov nor-mality tests were all significant (p < .001) showing that none of the factors was normally distributed. Transforming them did not improve their skewness or normalize the distributions, consistent with Tabachnick and Fidell’s (2007) assertion that transformations of data skewed in the same direction are unlikely to improve the distributions. The pattern of results was also similar using the transformed and un-transformed data, thus, only the latter results are presented here.
    A correlational matrix of the key study variables appears in Table 2. Younger age-group was significantly correlated with not having a reg-ular GP, no prior screening, and more psychological screening barriers and facilitators. Thus, age was controlled at step one of the logistic regression analyses. GP status was correlated with screening status, prior screening, and more psychological barriers. Thus, GP status was evaluated as a separate screening facilitator in the follow-up analyses of individual cervical cancer screening barriers and facilitators. Logistic regression analysis examined the women's screening status in relation to the number of psychological and practical barriers they identified, consistent with Hypothesis 1. Number of practical barriers and facilitators were expected to predict women's screening status more strongly than number of psychological barriers listed by women. Age
    Table 1
    Means and standard deviations of number of practical and psychological barriers and facilitators by age group (N = 338).
    Means (SD, Range) Means (SD, Range) Means (SD, Range)
    Table 2
    Spearman's Rho correlations between screening history, GP status, and cervical screening barriers and facilitator scores.
    Table 3
    Relationship between Number of Barriers and Facilitators and Screening Status (Up-To-Date vs. Overdue), Adjusted For Age Group (N = 338).
    Barriers:
    Age group
    Age group
    Facilitators:
    Age group
    Screening status was then examined in regard to the individual screening barriers and facilitators, using Chi-square tests. In Hypothesis 2, thorns was expected that a lack of time (practical barrier) and GP status (practical facilitator) would most strongly predict women's screening status. Results showed that the women who were overdue for screening were more likely to report embarrassment as a screening barrier. In contrast, women who were up-to-date with screening were more likely to report having a regular GP and the low cost of the test as screening facilitators. There was a trend for these women to endorse external screening reminders more often, see Table 4.
    Logistic regression analysis explored the relationship between prior screening and the number of psychological and practical screening barriers and facilitators. Age group was controlled at step one of the analysis, where it predicted 4.3% of variance in prior screening, χ2(1) = 11.01, p > .001. At step 2, the variables predicted 14% of variance in prior screening, R2 = 0.135: χ2(3) = 34.25, p < .001. The number of practical and psychological barriers both predicted prior 
    Table 4
    Individual Barriers and Facilitators in Relation to Screening Status (Up-To-Date vs. Overdue) (N = 330).
    Barriers %)
    Lack of time
    Cost
    practitioner
    screening, and practical barriers were the strongest predictor. Each individual practical barrier nearly tripled (i.e., 285%) the odds of having previously screened, whereas each psychological barrier in-creased the odds of never having screened by 33%, after controlling for age group. In the analysis of facilitators at step 1, age group predicted 4.3% of variance in prior screening, χ2(1) = 11.01, p > .001. At step 2, the variables predicted 8.1% of the variance in prior screening, R2 = 0.081: χ2(3) = 28.714, p < .001. Only the number of practical facilitators predicted significant variance in prior screening, after con-trolling for age group. There was a trend for older age to predict prior screening in women. Each individual practical facilitator more than doubled (223%) the odds of prior screening (see Table 5). Prior