## 5 Analysis and Findings
Figure 1 and Table 4 show the results of path analysis. In the analysis, modification indices were used to improve the model fit. Among demographic variables, only the path from age to turnover intention became significant (omitted in the figure). Regarding the relationship between the main variables, the only difference from \"China 2020\" shown by [PERSON] et al. [2] is that the path from anxiety to turnover intention has disappeared. Therefore, in China 2020, all eight hypotheses from H1 to H3 were supported, and in \"Wuhan 2023\", seven hypotheses except H1a were supported. This indicates the high applicability of [PERSON] et al. 's [2] model in different pandemic stages. As shown in [PERSON] et al. [2], here too, the relationship between anxiety and turnover intention is complicated. Anxiety has the effect of lowering turnover intention through an increase in COVID-19 compliance (\(\beta\) = 0.13 \(\times\)\(-\)0.19 = \(-\)0.02). However, at the same time, it has the effect of indirectly increasing turnover intention through the fatigue increase (\(\beta\) = 0.40 \(\times\) 0.33 = 0.13). As a result, the overall effect of anxiety on willingness to leave was positive (\(\beta\) = 0.11), indicating that anxiety enhanced the willingness to leave the job. However, in \"Wuhan 2023\", the overall effect of anxiety on willingness to leave was a little smaller than in \"China 2020\" (\(\beta\) = 0.13) because there is no longer a direct path from anxiety to intention to leave. However, it can be said that a common trend after the introduction of the regulation and after its abolition is that methods that arouse anxiety end up increasing the intention to quit the job. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Path** & & **Estimate** & & \(t\) \\ \hline  & & **China 2020** & **Wuhan 2023** & \\ \hline Anxiety & \(\longrightarrow\) & Compliance & 0.162 *** & 0.127 *** & 0.794 \\ Sex & \(\longrightarrow\) & Compliance & 0.045 *** & \(-\)0.035 & 3.086 ** \\ Tenure & \(\longrightarrow\) & Compliance & 0.050 *** & 0.026 & 0.918 \\ Social resources & \(\longrightarrow\) & Compliance & 0.129 *** & 0.297 *** & 5.728 *** \\ Psychological resources & \(\longrightarrow\) & Compliance & 0.308 *** & 0.379 *** & 2.597 *** \\ Anxiety \(\times\) Psychological resources & \(\longrightarrow\) & Compliance & \(-\)0.673 *** & \(-\)0.444 *** & 2.107 * \\ Anxiety & \(\longrightarrow\) & Fatigue & 0.346 *** & 0.405 *** & 1.260 \\ Social resources & \(\longrightarrow\) & Fatigue & \(-\)0.351 *** & \(-\)0.369 *** & 0.938 \\ Psychological resources & \(\longrightarrow\) & Fatigue & \(-\)0.090 *** & \(-\)0.078 * & 0.332 \\ Anxiety \(\times\) Psychological resources & \(\longrightarrow\) & Fatigue & \(-\)0.119 *** & \(-\)0.139 *** & 0.837 \\ Age & \(\longrightarrow\) & Fatigue & \(-\)0.077 *** & \(-\)0.046 & 0.904 \\ Tenure & \(\longrightarrow\) & Fatigue & 0.059 *** & \(-\)0.023 & 2.415 * \\ Compliance & \(\longrightarrow\) & Turnover intention & \(-\)0.275 *** & \(-\)0.190 *** & 1.659 \\ Sex & \(\longrightarrow\) & Turnover intention & \(-\)0.079 *** & \(-\)0.054 & 0.746 \\ Fatigue & \(\longrightarrow\) & Turnover intention & 0.241 *** & 0.327 *** & 2.306 * \\ Anxiety & \(\longrightarrow\) & Turnover intention & 0.114 *** & 0.008 & 2.817 ** \\ Tenure & \(\longrightarrow\) & Turnover intention & \(-\)0.073 *** & \(-\)0.177 *** & 2.842 ** \\ Age & \(\longrightarrow\) & Turnover intention & \(-\)0.051** & \(-\)0.105 *** & 1.494 \\ Social resources & \(\longrightarrow\) & Turnover intention & \(-\)0.219 *** & \(-\)0.197 *** & 0.033 \\ Social resources \(\times\) Compliance & \(\longrightarrow\) & Turnover intention & \(-\)0.260 *** & \(-\)0.270 *** & 0.305 \\ \hline \hline \end{tabular} Note: \(n\) = 2973 for “China 2020” and \(n\) = 813 for “Wuhan 2023”. *** \(p\) < 0.001, ** \(p\) < 0.01, * \(p\) < 0.05. \(t\): Student’s \(t\) value. The figures are standardized coefficients.  
\end{table} 
Table 3: Between-group comparison of paths.