**4. Results**

The first analysis sought to describe the sample based on demographic characteristics based on frequencies and summary statistics such as mean, median, and standard deviation.

Thus, **Table 4** describes the sample as a feminized population, while women represent 72%. Regarding ethnicity, most people identify as mestizos. Only 30% define themselves as white people. The percentage of black people or mulattos is less than 1%. This is different about other studies focused on social services, where the distribution of people according to their race presents greater dispersion than the case presented [38].

Other relevant information regarding the sample is its high level of education, while 94.7% have higher education courses. Moreover, 38.5% have postgraduate studies that far exceed the national reality. <sup>1</sup>

<sup>1</sup> In Chile, the academic undergraduate degree (between 4 and 6 years) is sufficient to practice professionally.


#### **Table 4.**

*Characteristics of the study sample.*

On the other hand, it is relevant to note that the people in the sample have a mean of 40.23 years old and 7.7 years of tenure, thus constituting a young population with average seniority higher than other studies in the same area. Finally, it is important to highlight the composition of the socioeconomic level of the sample since a large part of the people who participated in the study belongs to the middle and upper sections. Although this composition is different from that observed in the national population, it makes sense that people with a high level of schooling have a high average socioeconomic situation.

Finally, it should be noted that the sample presents a mean index of affectation of quality of life above the arithmetic mean of the scale. In addition, 50% of the sample has an index higher than 3, which can mean a high affectation.

After a description of the sample, bivariate analyses were performed to observe the relationship between demographic aspects that could be configured as antecedents of people's quality of life.

Thus, we analyzed the average affectation of quality of life by COVID-19 according to organization, gender, ethnicity, work modality, school level, and socioeconomic *The Effect of COVID-19 on the Quality of Life of Care Workers: Challenges for Social Services… DOI: http://dx.doi.org/10.5772/intechopen.105603*

level, identifying a relationship between affectation of quality of life according to the organization of those who responded, their age, level of schooling and socioeconomic level.

Based on these first results and regarding the objective of the study, we made contingency tables to evaluate the factors that show a relationship with the COV19- QoL index.

In the first place, we observed the age, as it presents a negative relationship with COV19-QoL, which in the framework of the sector and sample analyzed could be related to the care work that people could have, particularly women, younger, for this we categorize people in age ranges and gender. This analysis is presented in **Table 5**.

In fact, the group between 25 and 35 years old presents the highest levels of affectation, both in men and women, exceeding the average of the sample. Because one of the organizations has a higher percentage of men, we observe the same relationship


#### **Table 5.**

*COV19-QoL mean and age.*


#### **Table 6.**

*COV19-QoL and demographics variables.*

according to the organization, and the trend is maintained, that is, considering the gender and organization of people, people between 25 and 35 years old are the most affected.

On the other hand, we were particularly struck by the relationship observed between the organization and COVID-19 affectation, which is detailed in **Table 6**, despite the differences in mission, they are similar in relation to the type of services they offer and the work they do. Because of the above, we reviewed the timing of the data collection, because the study was conducted at a time when the pandemic was active, therefore, the specific COVID-19 situation could affect the average affectation. In fact, the organization that had an average affectation of 3.34, the highest, participated in the study when the infection positivity rate was 12%, while the organization that had the lowest affectation value participated in the study when the positivity rate was also the lowest (1%).

Finally, we analyze the results associated with the socioeconomic level and the negative relationship to COV19-Qol. Given the low representation of the endpoint levels in the sample, we categorized the variable into three: low, medium, and high. According to this classification, it became clearer that the most affected were middleclass people, regardless of organization and gender, as presented in **Table 7**.

However, considering this information and the results of the bivariate analyses, a bivariate correlation test was applied, obtaining the Pearson coefficient. The results are described in **Table 8**.

According to the results of the correlations, it is possible to confirm a positive relationship between socioeconomic status and the affectation of quality of life. Therefore, people from the lower ranges of socioeconomic levels have lower average


**Table 7.**

*Cov19-QoL mean by organization, gender, and socioeconomic level.*


#### **Table 8.**

*Correlations Cov19-QoL mean by organization, gender, and socioeconomic level.*

scores that show a lower affectation of COVID on quality of life. There is also a negative relationship between age and affectation, which means that older people have a lower level of affectation on quality of life. Finally, the direct relationship between test positivity and deterioration of quality of life was confirmed.
