Determining the cutoff points of the 5C scale for assessment of COVID-19 vaccines psychological antecedents among the Arab population: A multinational study (preprint)

ABSTRACT

Background: One of the newly faced challenges during the COVID-19 is vaccine hesitancy (VH). The validated 5C scale, which assesses five psychological antecedents of vaccination, could be effective in exploring COVID-19 VH. This study aimed to determine a statistically valid cutoff point for the 5C sub-scales among the Arab population. Methods: A cross-sectional study was conducted among 446 subjects from three Arab countries (Egypt, United Arab Emirates UAE, and Jordan). Information regarding sociodemographics, clinical history, COVID-19 infection and vaccination history, and 5C scale were collected online. The 5C scores were analyzed to define the cutoff points using the receiver operating characteristic curve (ROC) and to verify the capability of the questionnaire to differentiate whether responders are hesitant or non-hesitant to accept vaccination. ROC curve analysis was conducted setting for previous vaccine administration as a response, with the predictors being the main five domains of the 5C questionnaire. The mean score of each sub-scale was compared with COVID-19 vaccine intake Results: The mean age of the studied population was 37+11, 42.9% were males, 44.8% from Egypt, 21.1% from Jordan, and 33.6% from UAE. Statistically significant differences between vaccinated and unvaccinated participants, respectively, were detetd in the median score of confidence [6.0(1.3) versus 4.7(2.0)], complacency [(2.7(2.0) versus 3.0(2.0), constraints [1.7(1.7) versus 3.7(2.3)], and collective responsibility [6.7(1.7) versus 5.7(1.7)]. The area under the curve of the five scales was 0.72, 0.60, 0.76, 0.66, 0.66 for confidence, complacency, constraints, calculation, and collective responsibility at cutoff values of 5.7, 4.7, 6.0, 6.3, and 6.2, respectively. Conclusion the Arabic validated version of the 5C scale has a good discriminatory power to predict COVID-19 vaccines antecedent.