## 4 Results Analysis
### The Analysis of Pathways for Enhancing Psychological Crisis Intervention Resource Integration  
#### 4.2.1 Fuzzy-Set Calibration  
The fsQCA calibration is based on specific calibration functions, converting all the variable values into numbers between 0 and 1, and assigning membership scores to each case for each variable [62]. Referring to [PERSON]'s [48] research and following the mainstream QCA methodology, the direct method was used to calibrate the raw data into a fuzzy-set membership value matrix. Based on theoretical foundations and the characteristics of the sample data, the three anchor points for the six antecedent variables and one outcome variable (integration efficiency) were set as follows: \"full membership\" (0.95), \"crossover point\" (0.5), and \"full non-membership\" (0.05). According to the calculation process of the fsQCA method, the data calibration was performed using version 4.1 of the fsQCA software. Through the software, the comprehensive efficiency of psychological crisis intervention resource integration can be calculated, which then helps identify the configurations that influence the efficiency of psychological crisis intervention resource integration. Following [PERSON]'s recommendation, the consistency threshold for the solution was set at 0.8. The dataset file must be named with a.csv extension to be processed in the software. The results obtained after the calibration are shown in Table 4.  
2.2 The Analysis of the Necessity of Psychological Crisis Intervention Resource Integration Efficiency  
Before conducting the truth table analysis, it is necessary first to test the necessity of the condition variables. Typically, the standard for measuring the consistency of a necessary condition is relatively uniform: if the consistency exceeds 0.9, then the condition is considered necessary [48]. In the results of the configurational analysis for high and non-high psychological crisis intervention resource integration efficiency, the consistency of each antecedent condition is less than 0.9 (as shown in Table 5), thus not constituting a necessary condition. This indicates that medical institutions and other sectors do not necessarily need to improve the efficiency of psychological crisis intervention resource integration through any specific variable. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Variable Type**} & \multirow{2}{*}{**Variable Label**} & \multicolumn{3}{c}{**Anchor Point**} \\ \cline{3-5}  & & **Full Membership Point** & **Crossover Point** & **Full Non-Membership Point** \\ \hline Outcome variable & DEA & 1.000 & 0.897 & 0.479 \\  & RL & 18.650 & 13.000 & 3.350 \\  & SS & 250.330 & 98.325 & 25.168 \\  & FW & 94.100 & 87.500 & 75.850 \\ Condition variable & RJ & 48.425 & 17.140 & 7.779 \\  & JM & 9.737 & 6.235 & 3.365 \\  & CZ & 0.521 & 0.035 & 0.060 \\ \hline \end{tabular}
\end{table} 
Table 4: Calibration anchors for conditional and outcome variables. efficiency of psychological crisis intervention resource integration can be identified, as shown in Table 8.  
As shown in Table 8, in the analysis of pathways for integrating psychological crisis intervention resources efficiently in response to major emergencies and disasters, there are three condition configurations driving efficient integration, labeled as H1, H2, and H3. This results in three equivalent driving pathways, detailed as follows:  
Pathway one: policy support-driven path (H1). In this pathway, due to a lack of professionals and insufficient community collaboration, the primary method to enhance the efficiency of psychological crisis intervention resource integration relies on government financial support in disaster response and risk management. Adequate funding enables the procurement of necessary resources, the hiring of professionals, and supports intervention programs, building a robust service network for quick resource mobilization during major emergencies. Pathway two: professional capability-driven path (H2).