## 5 Analysis and Findings  
All statistical analyses were performed using IBM SPSS Statistics/AMOS Version 26 (IBM Corp., Armonk, NY, USA). Before proceeding to the main analyses, [PERSON]'s single-factor analysis was used to check whether the variance in the data could be largely attributed to a single factor, while confirmatory factor analysis (CFA) was used to test whether the factors were related to the measures. First, the factor analysis indicated that only 36.3 percent of the variance could be explained by a single factor, which was \(<\)50 percent. Thus, it was established that the data did not suffer from common method variance [78]. Next, for CFA, the model fit was evaluated by examining the chi-square (\(\chi^{2}\)), comparative fit index (CFI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA). Values above 0.95 are deemed to indicate a good fit for CFI, and values below 0.05 and 0.08 indicate a good fit for RMSEA and SRMR, respectively [79; 80]. Similar to [PERSON] et al. [2], it was shown that the 6-factor model (\(\chi^{2}\) (615) = 1073.550, \(p\) < 0.001; CFI = 0.986; RMSEA = 0.030, \(p\) < 0.001, 90% CI = 0.027-0.033; SRMR = 0.038) fits better than the 1-factor model that added 6 variables (\(\chi^{2}\) (657) = 2735.487, \(p\) < 0.001; CFI = 0.935; RMSEA = 0.062, \(p\) < 0.001, 90% CI = 0.060-0.065; SRMR = 0.085). Table 1 shows the results of the descriptive statistics. Looking at the \"Wuhan 2023\" results shown on the right side of the table, the highest score was 4.39 for psychological resources, followed by 4.38 for compliance and 4.08 for social resources. In contrast, at 1.87, the turnover intention was the lowest, followed by 2.27 for fatigue and 3.36 for anxiety about COVID-19. It can be said that overall positive awareness is high and negative awareness is low. However, looking at the standard deviation, the former is 0.78 to 0.86, while the latter is around 1.14, indicating that the latter has more variation. Therefore, it should be noted that negative consciousness, especially turnover intention, is not so high on average, but the difference between employees is relatively large. These results are also the same for \"China 2020\" shown on the left side of the table. Next, we will compare \"China 2020\" and \"Wuhan 2023\" using Student's \(t\)-test from the same table. Social resources are higher in \"Wuhan 2023\" than in \"China 2020\". On the other hand, fatigue, anxiety, and compliance are lower in \"Wuhan 2023\" than in \"China 2020\". The results were the same in an analysis of covariance (ANCOVA) that controlled for demographic variables age, tenure, manager, and sex (available upon request). These results indicate that \"Wuhan 2023\" has lower fatigue and anxiety than \"China 2020\", as well as lower awareness of workplace infection prevention measures, but higher social resources. There were also significant differences between the two groups in terms of age, tenure, and manager (all at the 0.1% level). These differences may reflect differences in time and location, as well as the fact that \"Wuhan 2023\" targeted a single company, whereas \"China 2020\" targeted multiple companies. Table 2 shows the results of the correlation analysis for each group. The bottom left of the table shows the results for \"China 2020\" and the top right shows the results for \"Wuhan 2023\". Table 3 shows the results of a simultaneous multi-population analysis conducted to examine the differences in the magnitude of the paths between variables in both groups. First, the negative path from anxiety to turnover intention is significantly larger in \"China 2020\" than in \"Wuhan 2023\" at the 1% level, while the negative path from fatigue to turnover intention is significantly larger in \"China 2020\" than in \"Wuhan 2023\" at the 1% level. On the other hand, the path to compliance for psychological resources and social resources was shown to be smaller in \"China 2020\" than in \"Wuhan 2023\" at the 0.1% level.