1. Introduction
In the last decades, the job market has undergone profound changes. Globalization, relocation of companies’ production, the introduction of technology and automation, and successive economic crisis have reshaped the employment landscape, creating giving rise to higher levels of unemployment. On the other hand, the growing globalization and the accelerated pressure of competition lead companies to find new, more flexible ways to respond to market fluctuations. In this context, there was a need to make the workforce more flexible, increasing short-term contracts, with the emergence of temporary workers in particular (De Cuyper et al. 2009).
Temporary work is a form of employment contract where the worker and the company agree on a specific duration of the employment relationship. It may assume several forms of work arrangements such as fixed-term, project-based, or seasonal contracts (ILO 2022b). Temporary workers already represent an important part of the workforce in Europe. In 2020, in the European Union (27 countries), temporary work represented 13.5% of the employed population. Nonetheless, the average of the last 10 years (2011 to 2020) is 15.1%, and over the years, in this period, this value has suffered few fluctuations (EUROSTAT 2021). The decrease seen in 2020 was due to the pandemic that led thousands of temporary workers to unemployment.
Despite representing an important part of the workforce, temporary work is considered one of the most insecure forms of employment contract in Europe (Eichhorst and Tobsch 2017). Temporary work is often synonymous with low-level occupations and poor career prospects (Mitlacher 2008). It is also synonymous with low wages and weak benefits. As such, temporary work is often associated with precarious work and living situations (Wagenaar et al. 2012; Dütsch 2011). In addition, temporary workers have access to less training and support in terms of occupational and health promotion activities (Becker and Engel 2015), often working in unsafe conditions and exposed to greater stress (De Cuyper et al. 2009; Silla et al. 2005).
Furthermore, temporary workers are exposed to greater risks in terms of mental health, and there is a growing concern with this type of employment. Several studies seek to assess how the conditions of temporary workers affect their attitudes at work, namely in terms of engagement (Virtanen et al. 2005; Wilkin 2013), their perceptions of mental health, burnout and anxiety (Eichhorst and Tobsch 2017), and the relationship between job satisfaction and mental health (Hünefeld et al. 2020).
Temporary agency work (TAW) is a specific form of multiple-party employment relationship characterized by a three-party relationship between the worker, the beneficiary company, and the employment agency (ILO 2022a), which is the focus of this research. In this context, the worker does not have a formal relationship with the user company, but with the employment agency, which in turn has a services contract with the user company. This type of triangular relation, where the worker performs the job on a company for a specific time period and has a formal contract with another company, forms a relational dynamic quite different from the more conventional and traditional forms of employment.
The employment situation and working conditions of TAW are reported to be similar to those of temporary workers in general. For example, lower job quality, defined by the nature of work and work environment, job prospects, participation in training, job security, and compensations, has been associated with TAW (Mitlacher 2008). Job insecurity and working conditions (two important job stressors) were found to be important factors conditioning TAW’s low job satisfaction in a systematic literature review of the past 16 years of research on job satisfaction and mental health (Hünefeld et al. 2020).
Despite several studies showing that organizational and work-related factors significantly influence workers’ mental health (Contreras et al. 2020; Zito et al. 2018), there is a lack of research on the importance of personal-related factors. According to the Job-Demands Resources (JD-R) model, workers may be exposed to job strain if work demands surpass the resources available to cope with those demands (Bakker et al. 2005). Moreover, the resources involved may be of different nature—organizational-, work-, or personal-related (Bakker and Demerouti 2007).
Taking into account the importance of temporary work employment in Europe, this research aims to understand the relevance of personal resources for temporary agency workers’ (TAW) mental health. Specifically, the aim is to examine the relationship of resilience (as a personal resource) with burnout, specifically in its three dimensions (emotional exhaustion, depersonalization, and personal achievement).
This research uses the JD-R model as a starting point. Specifically, it considers the importance of (personal) resources as a buffer of job demands, which is one of the basic assumptions of the JD-R model. The role of demands is encapsulated in the constraints posed by temporary work, namely precarious work (Wagenaar et al. 2012; Dütsch 2011), unsafe conditions, and greater stress (De Cuyper et al. 2009; Silla et al. 2005).
This research work’s novelty and contribution is related to the reinforcement of research on personal resources, much less studied than organizational and work-related resources (Ferreira and Gomes 2021), but it also contributes to the scarce literature on the mental health of temporary workers (Hünefeld et al. 2020). The main gap addressed is related to the focus on personal resources, specifically resilience, this research calls attention to the need to develop and reinforce individual skills and psychological traits capable of strengthening temporary workers’ mental health.
The remaining parts of the paper present the theoretical foundation of the research, followed by the details of the methods used to conduct the empirical study. Finally, the results are presented and discussed.
2. Theoretical Framework
According to the International Labour Organization (ILO 2022b), temporary work is a form of employment contract where the worker and the company agree on a specific duration of the employment relationship. It may assume several forms of work arrangements such as fixed-term, project-based, or seasonal contracts. Temporary agency work (TAW) is a specific form of multiple-party employment relationship characterized by a three-party relationship between the worker, the beneficiary company, and the employment agency (ILO 2022a), which is the focus of this research. In this context, the worker does not have a formal relationship with the user company, but with the employment agency, which in turn has a services contract with the user company. This type of triangular relation, where the worker performs the job on a company for a specific time period and has a formal contract with another company, forms a relational dynamic quite different from the more conventional and traditional forms of employment.
The JD-R model offers a framework for the explanation of workers’ attitudes and behaviors. The basic assumption is based on the relationship, and balance, between job demands and job resources (Bakker and Demerouti 2017). Every work or task presents challenges and obstacles that need to be overcome in order to obtain results. To accomplish the desired results, workers need to have access to the proper resources. When there is an imbalance between the demands and the resources available, workers will display positive or negative attitudes, and/or behaviors (Schaufeli et al. 2009). For example, the lack of resources to face job demands usually generates job strain that might lead to negative consequences for the worker, like burnout (Bakker et al. 2005; Bakker and De Vries 2021).
Being more vulnerable to mental health and well-being issues than permanent workers, like fatigue and depression (Hünefeld et al. 2020), TAW may lack the proper resources to cope with challenges and demands posed by their jobs. As a consequence of this tension between job demands and the lack of resources, TAW may be more prone to negative consequences, like emotional exhaustion, and as a consequence exhibit counterproductive behaviors (Striler et al. 2021).
Burnout is understood as one of the outcomes of job strain when job demands are higher than the resources available (Bakker et al. 2005). Although the study of burnout is not recent (Schaufeli et al. 2009), only in 2020 did the World Health Organization classify it as a syndrome “resulting from chronic workplace stress that has not been successfully managed”.
Although there is still no consensus about the conceptualization and measurement of burnout, there is some agreement that it is not a one-dimensional occupational phenomenon and that exhaustion is a core constituent of burnout (Demerouti et al. 2021). In fact, one of the first definitions highlights that burnout is the result of prolonged exposure to stressful and highly emotionally demanding work situations, and is characterized by high emotional exhaustion and depersonalization, and low personal accomplishment (Maslach and Jackson 1981). Emotional exhaustion is a feeling of lack of energy, resulting in psychological tiredness and difficulty in dealing with others’ emotions. Depersonalization denotes a lack of empathy, with individuals being more distant regarding their fellow colleagues or clients. Finally, (the lack of) personal achievement is related to one’s perceptions of being able to cope with work challenges and demands (Bakker and Demerouti 2007).
The preferred field of study for burnout has been human services in general, and the context of healthcare in particular (Schaufeli et al. 2009). The employment status of workers has not been considered a relevant context or variable in the literature.
A recent review conducted by Hünefeld et al. (2020) about job satisfaction and mental health of TAW included only six studies dealing with burnout, exhaustion, and fatigue. Of these, only one approached burnout in its three-dimensional conceptualization, while two used emotional exhaustion, and three the concept of overall fatigue.
The few studies approaching mental health issues and the type of employment arrangement are not clear regarding the connection between burnout or related concepts, such as emotional exhaustion, and TAW (Hünefeld et al. 2020). For example, a study conducted among prison nurses in Portugal shows that the employment contract is a significant variable to explain the levels of burnout, with permanent workers presenting higher levels of emotional exhaustion than temporary workers (Mendes et al. 2014). On the contrary, a study with the Dutch working population holding a permanent or temporary contract found that TAW presented higher levels of emotional exhaustion when compared with permanent and on-call workers (Wagenaar et al. 2012).
Despite this unclear relationship, TAW’s mental health state seems to be associated with the presence (or absence) of several resources and demands, and the interplay between them. Giunchi et al. (2016) in a study of Portuguese TAW found that perceived job insecurity—a job demand (Schaufeli and Taris 2014)—had a positive direct impact on emotional exhaustion. In another example, the work of Vahle-Hinz (2016) highlights the role of task- and employment-related resources and demands on well-being. In line with the JD-R model assumptions, he found that task- and employment-related demands were negatively associated with well-being, while employment-related resources were positively associated with well-being. Finally, and more recently, another study corroborated the moderating role of burnout’s three dimensions in the relationship between quality of working life factors, such as safe work environment and occupational healthcare (a form of job and organizational resources), and productivity (Leitão et al. 2021).
The interplay between demands and resources is well illustrated by the work of De Cuyper et al. (2009), namely that in the presence of certain demands, certain resources become more meaningful. They found in a sample of Belgians and Finnish temporary workers that autonomy (a job resource) had no influence on job involvement, but when workload (a job stressor or demand) was introduced as a mediator, the relationship between autonomy and job involvement became positive and significant.
Although the relationship between resources and mental health and well-being does not seem to be in dispute, most of the research tends to focus on job and/or organizational resources, rather than on personal resources (Britt et al. 2021; Schaufeli and Taris 2014). Despite this, it should be mentioned that the work of Xanthopoulou et al. (2007) is an important landmark in establishing personal resources as an important element of the JD-R model, by showing that self-efficacy, optimism, and organizational-based self-esteem make a significant contribution to well-being outcomes, such as emotional exhaustion. Other studies have focused on personal/psychological resources such as mindfulness (Janssen et al. 2020), psychological capital (Grover et al. 2018), self-esteem, and optimism (Huang et al. 2016). However, when focusing on temporary work arrangements in general, or in TAW in particular, there is no mention of personal resources in the literature, as far as the authors are aware.
The few studies that build on the concept of personal resources follow the Conservation of Resources Theory (COR) and the work of Hobfoll (2002). In this context, personal or psychological resources are conceptualized as cognitive features of an individual and the perception of an ability to control the surroundings. In line with the assumptions of Hobfoll (2002), mental resilience translates into a process of adaptation, especially when facing significant sources of stress, such as tragedy, trauma, threat, or adversity (Campbell-Sills and Stein 2007). This process of adaptation in the face of adverse situations presupposes that resilience will only be observable when the individual is faced with such situations (Earvolino-Ramirez 2007).
Despite organizational and job resources having been extensively explored in the literature (Bakker et al. 2005; Schaufeli et al. 2009; Bakker and De Vries 2021), there is a lack of research on the role of personal resources (Britt et al. 2021; Schaufeli and Taris 2014) in mental health and well-being, especially burnout. Moreover, it is consensual that temporary work arrangements are fertile ground for precarious and insecure working conditions (Eichhorst and Tobsch 2017), with poor career prospects (Mitlacher 2008), and less access to training and support in terms of occupational and health promotion activities (Becker and Engel 2015). This context can be favorable to more job strain and stress, which in turn may give rise to mental health risks (De Cuyper et al. 2009; Silla et al. 2005), such as burnout. Taking the JD-R model as a starting point, specifically the role of resources as a buffering effect, and the lack of research on the role of personal resources, this research will examine the relationship of resilience and burnout, as a three-dimensional construct in TAW.
The following hypotheses are raised and presented in Figure 1:
More resilient temporary workers have lower emotional exhaustion levels.
More resilient temporary workers have lower levels of depersonalization.
More resilient temporary workers have a higher level of personal accomplishment.
3. Method
This study is correlational and cross-sectional, using a quantitative methodology. The characteristics of the theoretical model and the hypotheses require validation only possible through extensive data collection and statistical testing.
3.1. Instrument and Measures
The questionnaire is made of three sections. The first section includes the items of burnout; the second section measures resilience; and finally, the third section includes sociodemographic questions (gender, age, district of residence, industry, and duration of the last (current) employment contract).
Taking into account the structural model, the variables under analysis are resilience (independent variable) and the three burnout dimensions—emotional exhaustion, depersonalization, and personal achievement (dependent variables). Burnout was measured using the abbreviated version of the Maslach Burnout Inventory (Riley et al. 2017), with nine items (e.g., “I feel emotionally drained from my work”). This short version comprises the original three dimensions of burnout measured by three items each. The scale presents good validity and reliability when compared to the original 22-item Maslach Burnout Inventory (Maslach and Jackson 1981). The items were measured by a seven-point Likert scale, ranging from “never” to “every day”. Resilience was measured by the short version of the Connor-Davidson Resilience Scale (CD-RISC-10) with 10 items (“I’m able to adapt to change”) using a five-point Likert scale (“1 = never true” to “5 = always true”) (Campbell-Sills and Stein 2007). We pre-tested the sample with 50 observations and a Cronbach-alpha of 0.86 was obtained, revealing the good internal consistency of the sample.
3.2. Participants and Sample Description
This study focuses on Portuguese workers with a temporary work employment contract at the time of the study. In Portugal, temporary workers represented about 17.8% of the total number of employees in 2020. However, in the last 10 years, the average was 21.2%, and only the year 2020 presented a percentage below 20%. This means that Portugal has one of the highest average rates of use of temporary work in Europe, only lower than Montenegro (28.5%), Poland (25.5%), and Spain (25.1%) (EUROSTAT 2021). The official figures in Portugal on the number of temporary workers reported 103.623 temporary agency workers (GEE 2022). Also, there are 368 temporary work agencies operating in Portugal (27 more than in 2016), and their revenue increased by 39% in the same period (INE 2022).
A sample of 2050 individuals participated in the study. Responses were collected through an online questionnaire sent to Portuguese temporary workers in March 2021. The questionnaire was sent to active temporary workers registered in temporary agencies. The sample is evenly divided in terms of gender (49.9% are men and women (50.1%), and 50.2% of temporary workers are under 30. In terms of the length of the last contract, 53.4% have a contract of more than 6 months, 22.7% between 3 and 6 months, and 23.9% for less than 3 months (Table 1).
3.3. Statistical Analysis
First, a statistical analysis was performed on the sample and then, the Partial Least Squares (PLS) method was used in the Smart PLS 3.0 software (Ringle et al. 2015). As the data were obtained through questionnaires, having collected several indicators that are grouped by latent variables and there was no normal distribution of data, according to Ringle et al. (2019), PLS was the most suitable method. This method does not assume data normality and allows a multi-statistical analysis, combining factor analysis with regressions.
The application of the PLS method presupposes two phases (Ringle et al. 2015): (1) application of the PLS logarithm to the structural framework and validation of the model obtained and (2) bootstrap analysis that allows testing the formulated hypotheses through the estimation from a regression by Ordinary Least Square.
Figure 2 shows the PLS model obtained from the application of the PLS algorithm to the Structural Framework. The connections established between the latent variables (represented in the circles) are called outer loadings. In the rectangles, the indicators that measure the latent variables are represented.
After obtaining the PLS model, it lacks validation in terms of predictive precision, reliability, and discriminant validity (Table 2). The predictive precision of the model is evaluated through the R2 that appear in the circles of the latent variables. According to Cohen (1988), the R2 of the variables BRN-EE (0.055) and BRN-DP (0.08) have a “small” effect and the variable BRN-PA (0.201) has a “medium” effect. The model’s reliability is evaluated through three indicators: Cronbach’s Alpha, composite reliability, and Average Variance Extracted (AVE). The first two indicators have 0.70 as reference values and the AVE 0.50, according to Hair et al. (2019). As shown in Table 3, the company presents internal convergence and is “satisfactory to good”, presenting values for the three indicators higher than the reference ones. Finally, the estimated PLS model has discriminant validity, which is evaluated by the Fornell–Larcker criterion, since the average variance extracted from the latent variable found on the diagonal marked in Table 2 is higher than the square correlations with the other variables latent.
4. Findings
The first set of results is related to the resilience variable statistics. Table 3 shows the mean and standard deviation, as well as the Cronbach alpha, which shows that this variable has good internal consistency.
The second set of results reveals the levels of burnout among temporary workers. The calculation of the levels followed (Shaikh et al. 2019): for emotional exhaustion and depersonalization, values from 0–9 were labeled as “no to low burnout” and values from 10–18 were labeled as “moderate to high burnout”; personal accomplishment is measured in a reverse was, so 0–9 was labeled as “moderate to high burnout” and 10–18 was labeled as “no to low burnout”.
The categorization of the levels of burnout is presented in Table 4.
Here, 65.3% of temporary workers present “no to low” personal accomplishment, although they present “no to low” emotional exhaustion (89.8%) and “no to low” depersonalization (94.8%). The second phase of the PLS method is the performance of a bootstrap analysis that allows testing the formulated hypotheses through the estimation of a multiple linear regression by Ordinary Least Square. The results of this analysis are shown in Table 5.
The results show that, as expected, the three hypotheses were validated. Temporary workers with higher levels of resilience have lower levels of emotional exhaustion (β = −0.234) (H1), lower levels of depersonalization (β = −0.088) (H2), and higher levels of personal accomplishment (β = 0.448) (H3).
5. Discussion
The goal of this study was to examine the relationship of personal resources—in the form of resilience—with burnout, as a mental health consequence of the work tension generated by temporary work. According to the JD-R model, the precarious situation of temporary workers can reduce the amount of available resources and lead to mental health problems (Hünefeld et al. 2020; Striler et al. 2021).
Data were collected from TAW and allowed the categorization of these workers’ burnout levels in the three dimensions (emotional exhaustion, depersonalization, and reduced personal accomplishment). TAW surveyed presented low levels of emotional exhaustion and depersonalization but reduced levels of personal accomplishment. According to Maslach and Jackson (1981), when workers have prolonged exposure to stressful situations and high emotional demands, they present high levels of emotional exhaustion and depersonalization and low levels of personal accomplishment. The present findings reveal that the levels of emotional exhaustion, depersonalization, and personal accomplishment are relatively low. While low levels of emotional exhaustion and depersonalization reveal a low risk of burnout, low personal accomplishment is an indicator of burnout.
The assumption that temporary workers may be exposed to greater mental health risks (Chambel and Farina 2015; Eichhorst and Tobsch 2017) would imply that burnout levels would be higher than those verified in the present research. Nevertheless, the low level of general burnout is in line with the inconsistency demonstrated by the previous literature regarding the mental health and well-being of temporary workers (Hünefeld et al. 2020). Furthermore, and more importantly, low levels of emotional exhaustion and depersonalization may be explained by the positive relationship resilience has with these workers’ mental health, as postulated by the theoretical model. Resilience is a process of adaptation when facing significant sources of stress (Campbell-Sills and Stein 2007). As such, being able to adapt to the uncertainty experienced by the lack of job security, low wages, and poor work conditions, may contribute to explaining the low levels of emotional exhaustion and depersonalization. Also, being a temporary agency worker means that the work relationship with the user company is mediated by the agency, which may also contribute mitigate the effect of potential sources of stress. Another aspect to consider in the explanation of low levels of emotional exhaustion and depersonalization reported in this study is that resilience and its effects are observable, especially in the face of adverse situations (Earvolino-Ramirez 2007). This means that if the strain is low, resilience will not be as visible as when facing highly stressful situations.
Low levels of personal accomplishment reported may be the reflection of several stressors usually found in this type of employment relation, such as job insecurity, precarious working conditions with low wages, weak social benefits, and lower levels of employment (Eichhorst and Tobsch 2017; Mitlacher 2008; Wagenaar et al. 2012; Dütsch 2011). Another explanation for the low levels of personal accomplishment may be found in the expectations (not fulfilled) of getting a permanent job by TAW (Clinton et al. 2011). Handy et al. (2020) investigated the mutual expectations of individuals involved in the triangular psychological contract of TAW (the worker, the agency, and the user company) and found that the expectations held regarding others involved in the relationship were quite high. This means that TAW may have high expectations regarding what the user company and even the agency may have to offer them. When these expectations are not met, the feeling of not achieving what they expected may explain the low level of personal accomplishment.
The findings supported all the hypotheses formulated and tested by the structural model. Resilience has a negative relationship with depersonalization and emotional exhaustion and a positive relationship with personal accomplishment. The magnitude of this influence shows that when the resilience of TAW increases by 1%, depersonalization decreases by 23.4%, emotional exhaustion decreases by 8.8%, and personal accomplishment increases by 44.8%. Thus, resilience is shown to have a moderate to weak negative correlation with depersonalization and emotional exhaustion, and a moderate to high positive correlation with reduced personal fulfillment. In this way, the results demonstrate the possible importance of resilience, as a personal resource, in reducing burnout (Ferreira and Gomes 2021).
According to what was already discussed regarding the context of temporary work, there are several factors that may strongly contribute to emotional exhaustion and depersonalization. For example, reduced levels of job support, low workplace fairness, job insecurity, low salary and social rewards, and high workload can develop emotional exhaustion and depersonalization (Striler et al. 2021; Aronsson et al. 2017). The prevailing and continued presence of these factors may weaken the positive role of resilience in reducing burnout. In the same way, other sources of strain, such as less favorable working conditions, imbalance between skills or education and work obligations, overwork, lack of support or resources, organizational disorder, and long working hours, negatively influence personal accomplishment (Hunter et al. 2019).
6. Conclusions and Practical Implications
The concern with the mental health of temporary workers has deserved special attention from academics but also from organizations that deal with this type of work. This concern results from the expressive representation of temporary workers in the active population and the working conditions associated with them, which may prove to be unfavorable for their well-being, with consequences in terms of mental health but also for performance and productivity. In this context, the objective of this study was to examine the importance of resilience, as a personal resource, with the formation of burnout as a consequence of the mental tension generated by the type of work arrangements.
The results showed that the resilience of temporary workers, as a personal resource, is correlated with burnout. In this way, it can inhibit the mental tension generated by the type of work of these workers, characterized by greater precariousness, risk and insecurity, lower wages, and lower social benefits. In this way, resilience can inhibit burnout, reducing emotional exhaustion and depersonalization.
The focus on resilience as a personal resource is aligned with the need to enhance employee self-care (Horstmann 2018), that is, the promotion of strategies that allow employees to make decisions about their health-related issues. However, despite the research aim and the results reported being focused on personal resources, specifically resilience, it does not reduce or obscure the importance of environmental variables, such as job and organizational resources. On the contrary, it exerts the important role personal resources should have in an effective and integrated well-being strategy promotion.
6.1. Practical Implications
In theoretical terms, this study contributes, in the context of the JD-R model, to a better understanding of the role of resilience, as a personal resource, especially as an inhibitor of burnout. In practical terms, as a personal resource, resilience can be learned (Atkinson et al. 2009; Edward and Warelow 2005), which offers the possibility of developing training and development strategies to strengthen TAW’s personal resources as a way to improve their mental health and, as such, their well-being.
6.2. Limitations and Future Research
The main limitations of this study are related to the use of only one personal resource. According to the literature, other personal resources should be considered in future research. The results of this study may differ if other personal resources were used and if an interaction between various personal resources were considered. Additionally, and following the assumptions of the JD-R model, several resources of different types (personal-, job-, and organizational-related) should be considered in order to develop a more accurate perspective of TAW’s situation.
The sociodemographic characteristics and contractual conditions (example: duration of contract) of temporary workers that may influence their personal resources were not considered in this study. This is due to the fact that the study focuses exclusively on resilience as a personal resource and its relationship with burnout. Consequently, the inclusion of personal or contractual differences was not considered in the study, that is, the consideration of aspects intrinsic to human nature and social aspects. The study was carried out with Portuguese temporary workers. The generalization of results to other work contexts and other geographies deserves additional care, namely within the framework of the context under analysis. Finally, there are some limitations derived from the methodology used: cross-sectional design, the risk of common method bias as all measures are self-reported and collected at one point in time.
Studies that consider the influence of the sociodemographic characteristics of temporary workers, as well as the contractual conditions and the associated regulations, on personal resources will allow strengthening the lines of research on this topic and developing practical implications for organizations that deal with temporary work (agencies and user companies) reduce burnout levels, increase the involvement of these workers with their work and, therefore, boost productivity. It would also be interesting to compare the results of this study with a similar study applied to workers with a permanent employment contract in order to analyze their differences and develop strategies for the type of employment contract to have lesser consequences on the mental health of their workers.
Conceptualization, P.F.; methodology, P.F. and S.G.; software, S.G.; validation, P.F. and S.G.; formal analysis, S.G.; investigation, P.F. and S.G.; resources, P.F.; data curation, S.G.; writing—original draft preparation, P.F. and S.G.; writing—review and editing, P.F. and S.G.; visualization, S.G.; supervision, P.F. and S.G. All authors have read and agreed to the published version of the manuscript.
Ethical review and approval were waived for this study due to Portuguese law and guidelines from the Foundation for Science and Technology.
Informed consent was obtained from all subjects involved in the study.
The data presented in this study are available on request from the corresponding author.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Sociodemographic Description.
| Frequency | Percentage (%) | |
|---|---|---|
| Gender | ||
| Male | 1023 | 49.9 |
| Female | 1027 | 50.1 |
| Age | ||
| Less than 30 years | 1030 | 50.2 |
| 31–40 years | 611 | 29.8 |
| 41–50 years | 307 | 15.0 |
| 51–60 years | 89 | 4.3 |
| More than 60 years | 13 | 0.6 |
| Duration of the last (current) contract | ||
| Less than 3 months | 490 | 23.9 |
| 3–6 months | 465 | 22.7 |
| More than 6 months | 1095 | 53.4 |
Validation measures of the PLS model.
| BRN–DP | BRN–EE | BRN–PA | RES | |
|---|---|---|---|---|
| Cronbach’s Alpha | 0.708 | 0.74 | 0.796 | 0.914 |
| Composite Reliability | 0.654 | 0.851 | 0.831 | 0.929 |
| Average Variance Extracted (AVE) | 0.527 | 0.656 | 0.622 | 0.573 |
| Fornell–Larcker Criterion | ||||
| BRN–DP | 0.654 | |||
| BRN–EE | 0.507 | 0.810 | ||
| BRN–PA | −0.022 | −0.085 | 0.789 | |
| RES | −0.088 | −0.234 | 0.448 | 0.757 |
Notes: Bold numbers are higher than the square correlations with the other variables latent.
Resilience statistics.
| Mean | Std Deviation | Cronbach Alpha | Obs. | |
|---|---|---|---|---|
| Resilience | 4.0231 | 0.77161 | 0.914 | 2050 |
Categorization of the levels of burnout.
| Burnout Dimensions | Mean ± SD, f (%) | Cronbach Alpha |
|---|---|---|
| Emotional Exhaustion (EE) | 3.48 ± 3.93 | 0.740 |
| No to Low (0–9) | 1840 (89.8%) | |
| Moderate to High (10–18) | 210 (10.2%) | |
| Depersonalization (DP) | 2.61 ± 3.32 | 0.708 |
| No to Low (0–9) | 1944 (94.8%) | |
| Moderate to High (10–18) | 106 (5.2%) | |
| Reduced personal accomplishment (PA) | 11.27 ± 4.80 | 0.796 |
| No to Low (10–18) | 1336 (65.3%) | |
| Moderate to High (0–9) | 714 (34.8%) |
Multiple linear regression results.
| Original Sample (O) | Standard Deviation (STDEV) | T Statistics (|O/STDEV|) | p Value | |
|---|---|---|---|---|
| H1: RES → BRN − EE | −0.234 | 0.024 | 9.647 | 0.000 |
| H2: RES → BRN − DP | −0.088 | 0.026 | 3.355 | 0.001 |
| H3: RES → BRN − PA | 0.448 | 0.022 | 20.684 | 0.000 |
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