# Acceptance of Medical Treatment Regimens Provided by AI vs. Human
## 6 Experiment 3
### Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Effect** & **Boot Standard** & **Boot CI Lower** & **Boot CI Upper** \\  & **Value** & **Error** & **Limit** & **Limit** \\ \hline total effect & \(0.373\) & \(0.163\) & \(0.052\) & \(0.695\) \\ direct effect & \(-0.034\) & \(0.195\) & \(-0.419\) & \(0.351\) \\ mediated effect of experience & \(0.329\) & \(0.106\) & \(0.126\) & \(0.540\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Decomposition of the total effect, direct effect, and mediated effect. significant (\(\beta\) = 0.373, \(t\) = 2.287, \(p\) = 0.023), and when the mediating variable was put in, the difference in acceptance that was triggered by the treatment subject was no longer significant (\(\beta\) = 0.249, \(t\) = 1.492, \(p\) = 0.137), indicating that agency also had a fully mediated effect on the relationship between the treatment subject and acceptance. In addition, the effect of treatment subject on agency was significant (\(\beta\) = 0.610, \(t\) = 4.070, \(p\) < 0.001). Specifically, the participants rated human physicians higher on agency and agency was a significant positive predictor of acceptance (\(\beta\) = 0.205, \(t\) = 2.864, \(p\) = 0.046). Finally, we conducted a conditional process analysis. With treatment subject as the independent variable, acceptance as the dependent variable, experience mind capacities as the mediating variable, and regimen content as the moderating variable, the test was conducted using the Bootstrap method, as suggested by [PERSON] (2013). Model 8 was chosen (Model 8 assumes that the first half of the mediator model and the direct path are moderated), where the Bootstrap sample size was set at 5000 and a 95% confidence interval was selected using a bias correction method. The results of the analysis (see Table 5) showed that after putting the regimen content into the model, the product term of treatment subject and regimen content had a significant effect on both experience (\(\beta\) = \(-\)0.356, \(t\) = \(-\)1.189, \(p\) = 0.235) and regimen acceptance (\(\beta\) = \(-\)1.113, \(t\) = \(-\)3.405, \(p\) = 0.001). This suggests that regimen content not only had the ability to moderate the treatment subject's effect on acceptance, but also had the ability to moderate the treatment subject's effect on experience. Further a simple slope analysis showed that, as shown in Figure 3, the patients were more receptive to human physicians compared to AI in the human surgical options (simple slope = 0.519, \(t\) = 2.032, \(p\) = 0.043), while the patients were less receptive to human physicians compared to AI in the utilitarian surgical options (simple slope = \(-\)0.503, \(t\) = \(-\)2.076 \(p\) = 0.039). As can be seen in Figure 4, the patients rated their experience perception of human physicians higher when they were faced with humane regimens (simple slope = 1.638, \(t\) = 9.656, \(p\) < 0.001); when they were faced with utilitarian treatment regimens, the patients also rated their experience perception of human physicians higher than AI, although this effect was weakened (simple slope = 0.995, \(t\) = 5.542, \(p\) < 0.001). Through the above process, we verified the mediating role of experience mind capacities in the relationship between the treatment subject and acceptance, and that the first half of the model of this mediation and the direct path was influenced by the content of the regimen, actually obtaining the model results in Figures 4 and 5. Again, with treatment subject as the independent variable, acceptance as the dependent variable, agency as the mediating variable, and the regimen content as the moderating variable, the test was conducted using the bootstrap method, as suggested by [PERSON] (2012). Model 5 was chosen (Model 5 assumes that the direct path of the mediator model is mod