## 3 Results
### Artificial Neural Network Design and Architecture  
An ANN was designed consisting of an input layer, a hidden layer (consisting of seven units and the hyperbolic tangent activation function), and an output layer (consisting of two units, a softmax activation function, and the cross-entropy function error). The statistical program itself generated the number of neurons in the hidden layer, by trying to develop the minimum possible number of neurons or nodes to optimize the network. Figure 1 shows the connectivity between nodes arranged in three layers. In the training phase, the cross-entropy error was 48.215 and the percentage of incorrect predictions was 7%. The learning rule stopped after one consecutive step without reduction of the error rate. The type of training was batch and with an optimization algorithm based on the scaled conjugate gradient. The initial lambda value for training was 0.000005, the initial sigma value was 0.00005, and the interval offset was \(\pm\)0.5. Regarding the testing phase, the cross-entropy error was 10.190 and the percentage of incorrect predictions was 6.8%. Finally, in the holdout phase, the percentage of incorrect predictions was reduced to 2.6%. Table 3 shows the contribution of each of the factors and covariates (independent variables) to the dependent variable of the network through the importance indexes (scale from 0 to 1) and normalized importance (scale from 0 to 100%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \(\mathbf{N}\) & \(\mathbf{M}\) & SD & **Standard. Error Mean** & \(\mathbf{p}\)**-Value** \\ \hline \multirow{4}{*}{Resilience} & Absence & 36 & 16.53 & 2.94 & 0.49 & 0.047 \\  & Presence & 383 & 15.57 & 2.75 & 0.14 & \\  & & \multicolumn{4}{c}{Virus stress} & \\ \multirow{2}{*}{Resilience} & Absence & 127 & 16.13 & 2.87 & 0.26 & 0.019 \\  & Presence & 292 & 15.44 & 2.71 & 0.16 & \\  & & \multicolumn{4}{c}{Stress from colleagues} & \\ \multirow{2}{*}{Resilience} & Absence & 172 & 15.71 & 2.85 & 0.22 & 0.098 \\  & Presence & 247 & 15.61 & 2.73 & 0.17 & \\  & & \multicolumn{4}{c}{Student stress} & \\ \multirow{2}{*}{Resilience} & Absence & 131 & 15.76 & 2.98 & 0.26 & 0.579 \\  & Presence & 288 & 15.6 & 2.68 & 0.16 & \\ \multirow{2}{*}{Resilience} & Absence & 187 & 15.78 & 2.87 & 0.21 & 0.393 \\  & Presence & 232 & 15.55 & 2.7 & 0.18 & \\ \multirow{2}{*}{Resilience} & Absence & 87 & 15,69 & 2.87 & 0.31 & 0.886 \\  & Presence & 332 & 15,64 & 2.75 & 0.15 & \\ \hline \hline \end{tabular} Note: \(N\): participants; \(M\): mean; \(SD\): standard deviation; \(p\)-value: bilateral significance in Student’s \(t\)-test.  
\end{table} 
Table 2: Descriptive statistics and significance of Student’s \(t\)-test for resilience and stress associated with COVID-19. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Importance** & **Normalized Importance** \\ \hline Genre & 0.024 & 4.6\% \\ Civil status & 0.081 & 15.5\% \\ Children & 0.026 & 5\% \\ Age & 0.076 & 14.6\% \\ Experience & 0.049 & 9.4\% \\ Exhaustion & 0.054 & 10.4\% \\ Cynicism & 0.083 & 15.9\% \\ Efficiency & 0.522 & 100\% \\ Resilience & 0.053 & 10.2\% \\ COVID-19 stress & 0.032 & 6.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Importance of independent variables in the artificial neural network. Figure 1: Artificial neural network with predictive capacity for the dependent variable burnout syndrome. Note. Genre = 1: Female; Genre = 2: Male; Civil_status = 1: Single; Civil_status = 2: With partner; Civil_status = 3: Married; Civil_status = 4: Divorced; Civil_status = 5: Widowed; Children = 1: Yes; Children = 2: No; Burnout = 0: Absence of burnout; Burnout = 1: Presence of burnout. The predictive capacity of the model is shown in Table 4. A correct prediction percentage of 93% was observed in the sample used for the training phase, 93.2% in the testing sample, and 97.4% in the holdout sample. During the training phase, the model generated a predictive algorithm that was refined and perfected in the testing phase.