Version 17

Statistical analysis was performed using IBM SPSS Statistics 22.0 version 25 (IBM Corp., Armonk, NY, USA). Categorical variables are presented as frequencies and percentages, whereas continuous variables are reported as the means ± standard deviations (SDs) or as the medians and interquartile ranges (IQRs, 25th-75th percentile). The normality of the distribution of the variables was determined using the Kolmogorov-Smirnov (K-S) test. Normally distributed data were expressed as the means and SDs, while the medians and IQRs were used for nonnormally distributed data. Groups were compared with student’s t-test or Mann–Whitney U-test as appropriate. Correlation analysis was performed by the Pearson method or Spearman method. The chi-square or Fisher’s exact test was used to compare categorical variables. The odds ratios and their 95% confidence intervals (ORs, 95% CIs) were calculated to show the associations between each predictor and the risks for clinical outcomes. The receiver operating characteristic curve (ROC) and the area under the curve (AUC) were calculated by MedCalc version 17 (MedCalc Software, Mariakerke, Belgium). The cut-off point of the ROC was also calculated to obtain the sensitivity and specificity of the model. All tests were two-tailed, P < 0.05 was considered statistically significant, and P < 0.01 was considered extremely significant.

We analyzed the data using IBM SPSS Statistics software, version 25 (IBM Corp., Armonk, NY, USA) and MedCalc version 17 (MedCalc Software, Mariakerke, Belgium). Categorical variables were presented as frequencies and percentages, whereas continuous variables were reported as means ± standard deviations (SD), or as medians and interquartile ranges (IQRs, 25th–75th percentile). We used the Chi-squared or Fisher’s exact test to compare categorical variables. The normality of the distribution of the variables was determined using the Shapiro–Wilk test. Normally distributed data were expressed as means and standard deviations, while medians and IQRs were used for non-normally distributed data. A t-test or one-way analysis of variance and the Mann–Whitney U-test were used to compare continuous variables. Using two-way analysis of variance, we assessed the sleepiness and depression score gaps among EM residents across varying residency years, and according to their gender. Univariate and multivariate regression analyses were used to identify risk factors for sleepiness or depression rates among the study participants. The odds ratio (OR) and 95% CI were estimated. All tests were two-tailed, and a p-value of <.05 was considered to indicate statistical significance.