All data analyses were performed using the Statistical Package for the Social Sciences, version 21, for Mac (IBM Spain, Madrid, Spain). The descriptive analysis of the data included the means and standard deviations (M + SD) for the quantitative variables, while frequencies and percentages were used for the nominal variables. Pearson’s tests and t-tests were used, respectively, to compute the correlations and to assess gender differences for the quantitative variables. Cohen’s d was used to test the effect size. Independent hierarchical multiple regression models were also applied to examine the effects of burnout (Step 1) and social support (Step 2) on health among health care professionals. The detection of multicollinearity was performed using the Variance Inflation Factor (VIF), with VIF > 5 as the cut-off point for the diagnosis of collinearity (Sheather, 2009). For multiple regressions, the R2 was obtained. Additionally, residual plots were used to assess the goodness of fit for the regression model. Finally, the indirect effects of social support on the effects of the three dimensions of burnout on health among health care professionals were examined using the bootstrap method via Process macro version 3.3 (Hayes, 2017) for SPSS and the interaction effects between gender. The number of bootstrap samples was set to 10,000. Baron and Kenny’s (1986) mediational triangle was also used to visually display the mediation effects. The general significance adopted was p ≤ 0.05.

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