We collected primary data from respondents directly through a two-part structured questionnaire. This was a cross-sectional study with purposive sampling. The first part consisted of demographic data collection, such as age, sex, race, marital status, comorbidities, highest education attained, monthly expenses, previous exposures or any close contact with COVID-19 patients, and whether respondents have any health insurance. The latter part of the questionnaire includes respondents' stance on vaccination before coming for a jab and their psychology measurement described below.
We included adults (> 18 years) who were vaccinated with CoronaVac (Sinovac Life Sciences, Beijing, China) in Puskesmas Putri Ayu, one of the biggest Puskesmas in Jambi city, Indonesia. Puskesmas are government-mandated community health clinics spread throughout Indonesia to promote primary prevention and healthier lives. Data collection was done from the 15th of March to the 25th of March 2021. Our exclusion criteria were broadly categorized into two, which were refusal to participate and contraindicated to COVID-19 administration. Due to the dynamic nature of clinical research and findings of COVID-19 vaccination, guidelines about who can be vaccinated were updated frequently, either by the government or Indonesian medical institutions. Therefore, we adhered to the Indonesian Society of Internal Medicine's recommendation (the 18th of March, 2021), which was the first to issue a recommendation about who could be vaccinated [15]. Patients with primary immunodeficiency, acute and active infections (including SARS-CoV-2 infections or 3 month post-infection), presented with a severe allergic reaction or anaphylaxis after the first dose of COVID-19 jab, blood pressure of ≥ 180/110 mmHg, unstable or uncontrolled chronic conditions, such as diabetes mellitus or heart failure, and those with Fatigue, Resistance, Ambulation, Illness, and Loss of weight (FRAIL) score of > 2 were contraindicated to COVID-19 vaccination. Although this recommendation specified that only 18–59 years should be vaccinated, on the 5th of February 2021, Indonesia's Food and Drug Administration issued an emergency use authorization that elderly (≥ 60 years) were eligible for vaccinations upon passing medical screenings [7]. Therefore, the elderly were also included in our study.
Respondents were classified according to their stance on COVID-19 vaccination. There was a question that went as follows: “Before coming to Puskesmas Putri Ayu, are you sure that you are ready to be vaccinated?” If respondents answered yes, they were classified as “vaccine acceptance”, no meant “vaccine-resistant”, and maybe meant that they were “accine-hesitant”.
Personality traits were assessed using The Big-Five Inventory (BFI-10). This inventory measured openness to new experiences, conscientiousness, extraversion, agreeableness, and neuroticism. Two items on a five-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree”, are used to assess each attribute [16]. We used the translated and validated BFI-10 in the Indonesian language [17]. Internal reliability coefficients were not assessed because the scale only used two items to evaluate each personality trait. A study found that coefficient alpha was inaccurate for proving internal consistency in this situation [18].
We also assessed analytical or reflective reasoning with the help of The Cognitive Reflection Task (CRT), a three-item analytical reasoning test in which participants were asked to solve logical issues that imply intuitively attractive but erroneous answers [19].
Finally, respondents were asked to rate their trust in the government (which consists of the government itself, the state, and the parliament), scientists, physicians, and other health professionals. On a five-point Likert scale, responses ranged from “do not trust at all” (1) to “totally trust” (5) [20].
IBM SPSS 26.0 (Statistical Package for the Social Sciences, IBM Corp., Armonk, NY, USA, 2019) was used for statistical analysis. Normality testing was carried out using the Kolmogorov–Smirnov test, and if the p value is more than 0.05, the data had a normal distribution. Presentation of data using mean and standard deviation implied that data were distributed normally, while median and range meant not normally distributed.
Although previous studies have validated the internal reliability of the questionnaires, Cronbach's α application was specific to a particular sample of respondents [21]. Therefore, its internal reliability needed to be assessed in our population as well. Taber [22] classified Cronbach’s α value into several categories, such as: excellent (0.93–0.94), strong (0.91–0.93), reliable (0.84–0.90), robust (0.81), fairly high (0.76–0.95), high (0.73–0.95), good (0.71–0.91), relatively high (0.70–0.77), slightly low (0.68), reasonable (0.67–0.87), adequate (0.64–0.85), moderate (0.61–0.65), satisfactory (0.58–0.97), acceptable (0.45–0.98), sufficient (0.45–0.96), not satisfactory (0.4–0.55) and low (0.11).
There were five categories for income. Poor is defined as whose household expenses per month are less than Rp 1,416,000 (~ $99); vulnerable is defined as whose household expenses per month are between Rp 1,416,000 to Rp 2,128,000 (~ $99–$148); aspiring middle class is defined as whose household expenses per month are between Rp 2,128,001 to Rp 4,800,000 (~ $148 to $334); middle class is defined as whose household expenses per month are between Rp 4,800,001 to Rp 24,000,000 (~ $334 to $1671); and upper class is defined as whose household expenses per months are above Rp 24,000,000 (~ $1671) [10].
Bivariate analysis was done using chi-square, independent t-test when data distribution was normal, and Mann–Whitney when data distribution was not normal. When p values are below 0.25, those indicators are included in multivariate logistic regression analysis. The performance of our final prediction results would be checked for discrimination using receiver operating curve (ROC) and calibration (goodness of fit) using the Hosmer–Lemeshow test [23]. Area under the curve (AUC) will be interpreted from ROC. When the ROC curve corresponds to random chance, AUC would be equal to 0.5, and when the ROC curve corresponded to perfect accuracy, AUC would be 1.0 [24]. A good calibration would be measured by a p value of > 0.05 [25].