Statistical analysis

The collected data were checked for completeness and accuracy before analysis and then coded and summarized in the master data sheet. All statistical analyses were performed with SPSS Software version 26.0 utilizing both descriptive and inferential statistics. For descriptive statistics, the frequency, percentage, mean, standard deviation, and range were calculated. The Kolmogorov–Smirnov test was used to evaluate the normality of the distribution of the outcome variables. Due to the nonnormal distribution, nonparametric tests (Mann–Whitney U and Kruskal–Wallis H) were used to compare means. Binary and multivariable logistic regression analyses were carried out to identify factors associated with nursing professionalism. Model fitness was assessed using the Hosmer–Lemeshow goodness-of-fit test (p = 0.83), which indicated a well-fitted model. Additionally, all variables satisfied the chi-square assumption, and their odds ratios were examined. To assess multicollinearity among continuous variables, variance inflation factor (VIF) values were computed and found within the acceptable range (1 to 2), confirming the absence of multicollinearity. Bivariate and multivariate logistic regression analyses were employed to identify factors associated with outcome variables. Variables with a p-value less than 0.2 in the bivariable analysis were included in the multivariable analysis. Significant associations with outcome variables were determined based on a p-value less than 0.05 with a 95% confidence interval.