**3. Results**

The 1176 participants of the present study were distributed between both genders, with 61.1% being female and 38.9% being male. Of these, 66.3% stated being responsible for dependents (older adults or children), whilst 33.7% did not. A total of 27.3% were categorized as having high resilience, 46.2% had moderate resilience, and 26.5% had low resilience. With regard to the question asking whether individuals in their immediate environment had contracted COVID-19, 42.7% indicated yes, whilst 57.3% stated that no. In reference to respondents' professional occupation prior to confinement, 34% were public employees, 22.3% were self-employed or lent their services to a private company, 21.8% were students, 18.5% reported studying and working, and 3.4% were neither studying nor working. With regard to their academic level, 50.1% reported their highest level of study being "studies of higher education", 29.3% had postgraduate qualifications, 11.1% had professional training, 6% possessed basic studies, and 3.7% had only third grade studies (Table 1)


**Table 1.** Descriptive characteristics of the sample.

Finally, with regard to working in emergency services, 27.9% reported doing so relative to 72.1% who did not. In relation to the time-period as it relates to the state of alarm, 61.8% of the questionnaires were completed during period 1 (from the 15–22 March) and 38.2% during period 2 (from the 23–31 March) (Table 1)

In the relational study of variables relating to the resilience level, statistically significant differences were found (*p* = 0.015) pertaining to sex. Specifically, low resilience was more common amongst females than males (29.1% relative to 22.5%), with these figures being inverted when high resilience was considered (31.3% for males and 24.9% for females). With regard to being responsible for dependents, differences were shown in the data (*p* = 0.000), with individuals responsible for dependents showing a greater prevalence of high resilience (35.1%) than those without this responsibility (23.3%).

No association was found (*p* = 0.248) with regard to whether respondents had individuals in their immediate environment who had contracted COVID-19, whilst participants' occupation prior to confinement did produce statistically significant differences (*p* = 0.001). Concretely, participants who were working as public employees, were self-employed, or worked for a private company obtained higher values, with 27.5% reporting a high resilience relative to just 16.4% of students who reported the same optimum level.

Regarding the academic levels, statistically significant differences (*p* = 0.001) emerged. In this case, 33% of respondents with postgraduate or doctorate studies reported high resilience, this being a greater percentage than the 18.6% of individuals with only basic studies who also obtained scores belonging to this category. In relation to whether or not individuals worked in a position related to emergency services, a statically significant association was found (*p* = 0.002). This was generated because those who did have a relevant profession (32%) presented a higher prevalence of high resilience than those who did not come into contact with emergency services through their work (25.5%). These results were inverted when considering low resilience, with 29.2% of those in contact with emergency services

falling into this category, relative to 19.5% of those not in contact. Finally, differences were not detected (*p* = 0.243) with regard to the period of study completion and resilience level. (Table 2)


**Table 2.** Associations between resilience and all other variables.

Note 1. Statistically significant differences at the level *p* < 0.05 \*.

Once the descriptive and relational study was determined, we proceeded to the second study objective, which was to establish a predictive model of high resilience through binary logistic regression. The variables describing close others with COVID-19 and the time-period (*p* ≥ 0.05) were excluded. In the first step of analysis, sex did not produce significant outcomes and so it was also excluded from the model. In the second step, good fit was shown through outcomes of the omnibus test (X<sup>2</sup> = 48.721; 4df; sig = 0.000), Hosmer–Lemeshow test (X2 = 4.095; 6df; sig = 0.664), Cox and Snell R<sup>2</sup> (0.041), and Nagelkerke statistic (0.059). The model adequately explained 72.7% of cases.

Likewise, as can be seen in the following table, the model identified associations (*p* < 0.05 in the adjusted regression model) between resilience and professional occupation (Exp [B]: 2.160 [1.504–3.101]), academic level (Exp [B]: 1.579 [1.089–2.290]), job related to emergency services (Exp [B]: 1.668 [1.242–2.239]), and responsibility for dependents (Exp [B]: 1.583 [1.194–2.097]) (Table 3).

**Table 3.** Binary logistic regression model.

