**3. Results**

Table 1 provides the descriptive statistics for the demographic variables to give the respondents' background characteristics. The average age of respondents is 38.9 years, with a standard deviation of 8.12. The results show that 36 (13.1%) respondents are less than 30 years of age, 186 (67.8%) are between ages 31 and 45, and 53 (19.3%) are 46 or older. For gender, the percentage of male respondents tends to be higher than that of female, which is 154 (56%) and 121 (44%), respectively. Additionally, most of the respondents (84.7%) are married or living together, 9.5% have never been married or single, while only 3.6% had earlier been married but then divorced or separated. The great majority of the respondents, 230 (83.6%), are citizens, while only 45 (16.4%) of them are non-residents or non-citizens. Regarding their education level, most of the respondents have attained two years of college education (42.6%), followed by those with bachelor's education (36.7%), 9.5% in graduate-level, and 7.6% attaining high school education or less. Regarding the experience with primary health centers (PHCs), the majority are those with more than ten years of experience (48.4%), 28.7% of the respondents have five years or less of experience, and those with 6 to 10 years of experience are only 22.9% of the respondents.



Table 2 shows the Pearson Correlation coefficients between the study's independent variables (conflict, ambiguity, overload, and social support) and the dependent variable of stress. All the independent variables in the study have a positive significant correlation with stress except overload, which has a significant negative correlation. The result was: conflict (*r* = 0.487, *p* < 0.01), ambiguity (*r* = 0.479, *p* < 0.01), overload (*r* = −0.332, *p* < 0.01), and social support (*r* = 0.090, *p* < 0.01). Additionally, conflict was found significantly correlated with ambiguity (*r* = 0.509, *p* < 0.01), overload (*r* = −0.206, *p* < 0.01), and social support (*r* = 0.079, *p* < 0.01). Ambiguity exhibited significant negative correlation with overload (*r* = −0.265, *p* < 0.01) and social support (*r* = −0.026, *p* < 0.01). Finally, overload had a negative significant correlation with social support (*r* = −0.013, *p* < 0.01). These results give a clear evidence that most of the independent variables exhibited significant correlation with each other.


**Table 2.** Pearson Correlation between stress and the study variables.

Note. \*\* Correlation is significant at the 0.01 level (2-tailed).

The study used a chi-square test to compare healthcare workers' demographics and working hours in the Fever Clinics to improve the understanding of their characteristics based on their stress level (Low, Moderate, and High). These results are presented in Table 3. The result showed a significant difference related to participants' age groups. Younger health care workers more likely to have higher stress than the other age groups. The analysis on workers' nationality showed significant differences; non-Saudi health care workers have higher stress than Saudis. Gender, marital status, and level of education did not show significant differences in their level of stress. Additionally, working hours change did not reveal any significant differences in the stress level among the Fever Clinics' health care workers.

**Table 3.** Demographic characteristics by stress levels.


Table 4 shows multiple regression analysis results between the criterion of stress and the predictors of conflict, ambiguity, social support, and overload. The multiple regression results indicated a significant collective effect between the independent variables of conflict, ambiguity, social support, and the dependent variable of stress. The overload was dropped from the model by the SPSS and it might be dropped because of using a single item to measure the overload. According to the result, the significant predictors based on their magnitude are: (ambiguity (*Beta* = 327, *p* < 0.001), conflict (*Beta* = 313, *p* < 0.001), and social support (*Beta* = −0.150, *p* < 0.01)). R square = 0.36, so all of these factors together explained 36.4% of perceived stress.


**Table 4.** Multiple regression analysis: predictors of stress.

*Note.* \*\* *p* < 0.01 level (2-tailed).
