## 5 Model Validation
### Indicator Weights  
The path coefficients in the model represent the significance of each influencing factor on traffic attraction. After normalization, the path coefficients can be interpreted as the weight coefficients of the related variables. Therefore, by calculating the weight of each variable, we can quantitatively analyze the influencing factors of outpatient traffic attraction. The formula for calculating the weight coefficients of the latent variables is as follows:  
\[W_{i}=\frac{t_{i}}{\sum\limits_{i=1}^{n}t_{i}} \tag{8}\]  
In the equation, \(W_{i}\) represents the weight coefficient of the latent variable (hospital size, medical level, and traffic accessibility): \(t_{i}\) is the path coefficient indicating the influence \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Hospital** \\ **Type** \\ \end{tabular} & **Path** & \begin{tabular}{c} **Standardized** \\ **Coefficient** \\ **(Std.R.W.)** \\ \end{tabular} & \begin{tabular}{c} **Standard** \\ **Error (S.E.)** \\ \end{tabular} & \begin{tabular}{c} **Critical** \\ **Ratio (C.R.)** \\ \end{tabular} & \(p\) & **Conclusion** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} general \\ hospitals \\ \end{tabular} } & traffic attraction \(\leftarrow\) hospital size & 0.276 * & 0.141 & 1.956 & 0.050 & Accepted \\  & traffic attraction \(\leftarrow\) medical level & 0.435 * & 0.118 & 3.697 & *** & Accepted \\  & traffic attraction \(\leftarrow\) traffic accessibility & 0.132 & 0.129 & 1.020 & 0.308 & Rejected \\ \hline \multirow{3}{*}{
\begin{tabular}{c} specialized \\ hospitals \\ \end{tabular} } & traffic attraction \(\leftarrow\) hospital size & 0.228 & 0.177 & 1.287 & 0.198 & Rejected \\  & traffic attraction \(\leftarrow\) medical level & 0.526 * & 0.186 & 2.828 & 0.005 & Accepted \\ \cline{1-1}  & traffic attraction \(\leftarrow\) traffic accessibility & 0.318 * & 0.156 & 2.044 & 0.041 & Accepted \\ \hline \hline \end{tabular}  
* Indicates a path significance level of \(p<0.05\). *** Indicates a path significance level of \(p<0.001\).  
\end{table} 
Table 5: Standardized path coefficients and hypothesis test results of the model. Figure 7: Path relationship and parameter calibration results of the traffic attraction model for specialized hospitals. of the latent variable on the attraction of traffic; \(n\) and \(i\) are the number of latent variables and the calculated index number, respectively. Equation (9) calculates the weights of the observed variables (such as construction area, number of beds, etc.) relative to the latent variables. Equation (10) represents the impact weight of the observed variables on traffic attraction:  
\[S_{j}=t_{i}\times t_{ij} \tag{9}\]  
\[Q_{j}=\frac{S_{j}}{\sum\limits_{j=1}^{n}S_{j}} \tag{10}\]  
In the equation, \(Q_{j}\) represents the path coefficient of the observed variable's influence on hospital traffic attraction; \(t_{ij}\) represents the path coefficient of the observed variable's influence on the latent variable; \(S_{j}\) represents the weight coefficient of the observed variable. The computed weight values are presented in Table 6.  
Taking general hospitals as an example for validation, the weights of hospital size and construction area are calculated according to Equations (8)-(10) as follows:  
\[W_{\text{hospital Size}}=\frac{t_{i}}{\sum\limits_{i=1}^{3}t_{i}}=\frac{0.28}{0. 28+0.44+0.13}=0.329 \tag{11}\]  
In the equation, \(W_{\text{hospital Size}}\) represents the weight coefficient of the latent variable \"hospital size\"; \(t_{i}\) are the path coefficients for the effects of hospital size, medical level, and traffic accessibility on the traffic attraction; \(n\) and \(i\) denote the number of latent variables and the calculated index number, respectively.