1. Introduction
In 2023, burnout levels had significantly increased compared to previous years, driven largely by telework conditions, rising job demands, and blurred boundaries between work and personal life (Costin et al.,2023; Eurofound, 2023). This trend has been further exacerbated by the COVID-19 pandemic, which transformed work practices, leading to the adoption of hybrid and remote work models. While these models initially seemed beneficial, they have introduced new challenges in managing employee well-being (Costin et al., 2023; Soto-Rubio et al., 2020). The lack of clear separation between work and personal life has contributed to heightened emotional exhaustion and work overload, two core components of burnout (Leiter & Maslach, 2024; Van Zoonen & Sivunen, 2021). Research indicates a strong correlation between burnout and negative affective responses (Khalkhali et al., 2024; Koutsimani et al.,2023)
As a result, workplace well-being has emerged as a critical focus within organizational psychology due to its direct impact on employee productivity, health, and overall quality of life. Studies show that promoting well-being at work enhances individual and collective performance and reduces absenteeism, turnover, and health-related costs (Lubbadeh , 2020). Psychosocial risk factors, engagement, and burnout are essential factors that significantly influence workers’ well-being (Bakker & Demerouti, 2017; Maslach & Leiter, 2016; Sinclair et al., 2024).
Psychosocial risk factors refer to conditions within the work environment that can lead to stress, such as excessive workload, lack of control over tasks, and poor interpersonal relationships. When these factors are not effectively managed, they can negatively impact employees’ physical and mental health, leading to symptoms such as anxiety, depression, and, ultimately, professional burnout (Demerouti et al., 2019; Leka & Nicholson, 2019; Leiter & Maslach, 2024). Burnout is a response to prolonged job stress, marked by decreased energy, emotional exhaustion, and a cynical attitude toward work, all of which diminish job performance and personal satisfaction (Leiter & Maslach, 2024; Maslach & Leiter, 2016). However, these symptoms are nonspecific and often overlap with other mental health disorders, making diagnosis challenging (Koutsimani et al.,2023; Parker & Tavella, 2021).
Conversely, engagement is a positive, fulfilling state of mind related to work, characterized by high energy levels, dedication, and absorption in daily tasks (Bakker & Demerouti, 2017). High engagement is linked to greater well-being at work, as engaged employees tend to experience higher satisfaction, better performance, and a lower likelihood of burnout (Sinclair et al., 2024). However, in environments where psychosocial factors are poorly managed, even highly engaged employees can suffer negative effects (Maung et al., 2023), highlighting the crucial role of a healthy work environment.
Understanding the interrelationship between psychosocial risk factors, engagement, and burnout is key to identifying the dynamics that influence workplace well-being. A work environment with high psychosocial risks can reduce engagement and increase the likelihood of burnout, ultimately impacting the health and performance of employees. Thus, effectively managing these factors is crucial to creating a work environment that promotes both well-being and productivity (Eurofound, 2020; Lubbadeh , 2020).
Considering the definitions provided and the potential relationships between variables in the workplace, the primary aim of this research is to assess burnout levels, occupational health (defined by the presence or absence of psychosocial risk factors), engagement, and well-being/job satisfaction within a sample of working adults from diverse demographic backgrounds. We expect to observe high current burnout levels and moderate well-being, health, and engagement (H1).
Furthermore, we aim to explore the relationships between the studied variables and the impact of occupational health (absence of psychosocial risks), burnout syndrome, and engagement on occupational well-being. We expect to observe relationships between all the variables, that is, inverse relationships between opposing variables and direct relationships between similar variables. Specifically, higher occupational health and well-being/job satisfaction will correlate with lower burnout and higher engagement, and vice versa (H2). Additionally, we predict that occupational well-being will be influenced by good occupational health (absence of psychosocial risk factors), low burnout, and high engagement (H3).
2. Materials and Methods
Participants
A total of 112 individuals of both genders, aged between 18 and 65 years, participated in this study after providing informed consent (see
Table 1 for sociodemographic details). The majority (63.4%) reported working between 31 and 40 hours per week, while 9.8% worked between 1 and 10 hours per week. Most participants (91.1%) were employed in the service sector.
Measures
The measurement scales used in this study, along with their key characteristics and psychometric properties, were as follows:
Developed by Maslach and Jackson (1981) and adapted to Spanish by Schaufeli et al. (2002), this scale assesses burnout syndrome through three key dimensions: Exhaustion, cynicism, and professional competence. It comprises 15 items, with five items measuring burnout, four measuring cynicism, and six measuring professionalcompetence. Responses are rated on a Likert-type scale from 0 (never) to 6 (always). High scores in burnout and cynicism, combined with low scores in professional competence, are taken to indicate burnout. Scores for each dimension are calculated by summing the items within each dimension and dividing by the number of items. Participants were classified into three levels of burnout—mild (burnout in one domain), moderate (burnout in two domains), and severe (burnout in all three domains). This scale demonstrates high validity and reliability, with an overall α=0.89 and subscale reliability ranging from 0.73 to 0.98.
Created by Blanch, Sahagún, Cantera, and Cervantes (2010), this scale consists of 55 items across six subscales: Affect (10 items), competence (10 items), expectation (22 items), somatization (5 items), burnout (4 items), and alienation (4 items). Responses are rated on a Likert scale from 1 (never) to 5 (always) in this study. The affect, competence, and expectation scales assess basic well-being, while the somatization, burnout, and alienation scales address collateral effects. Scores are calculated by summing the items in each subscale, with overall scores divided into basic well-being and collateral effects. This scale shows high validity and reliability, with an overall α=0.90 and subscale reliability ranging from 0.85 to 0.93.
The DECORE questionnaire, developed by Luceño et al. (2005, 2006), consists of 40 items rated on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree), assessing workers' perceptions of psychosocial risks. It includes four subscales: Organizational support (12 items), rewards (11 items), control (9 items), and cognitive demands (8 items). Total scores range from 40 to 200, with higher scores indicating a higher perception of psychosocial risk factors. This questionnaire demonstrates high validity and reliability, with an overall α=0.85 and subscale reliability ranging from 0.62 to 0.84.
Developed by Schaufeli, Salanova, González-Romá, and Bakker (2002), the UWES consists of 17 items measuring three engagement dimensions: Vigor (6 items), dedication (5 items), and absorption (6 items). Responses are rated on a Likert scale from 0 (never) to 6 (always). Scores for vigor, dedication, and absorption are obtained by summing the items within each dimension. This scale shows high validity and reliability, with an overall α=0.94 and subscale reliability ranging from 0.79 to 0.88.
Procedure
For the purposes of this study, we first created a questionnaire using Google Forms, which included the self-report measures mentioned earlier. The questionnaire consisted of three sections. The first section collected sociodemographic data, provided basic information about the study, and a consent statement outlining the voluntary nature of participation, the confidentiality of participant responses, and the absence of liability for participants. The second section included questions about burnout and general job satisfaction, while the third section focused on occupational health and engagement.
After the questionnaire had been created, it was distributed through social media platforms (Facebook, WhatsApp, Instagram, and Email), inviting workers from various sectors to participate. Participants were required to be of legal age and currently employed under a valid contract, regardless of the company or sector. They completed the measures individually in a single session.
This study adheres to the Declaration of Helsinki and does not involve medical experimentation, so it was not subject to approval by a local bioethics committee.
Statistical Analysis
This study adopted a cross-sectional correlational design.
To conduct the analyses presented in the study, we first performed preliminary and exploratory analyses. This step was necessary to detect and, if required, correct any data entry errors, missing values, or outliers and verify the assumptions for parametric testing.
The normality test revealed that most variables did not follow a normal distribution (Kolmogorov-Smirnov test, p<0.05). The Levene test confirmed the homogeneity of variances for most variables (p>0.05), allowing us to proceed with parametric tests for statistical analyses.
In addition to descriptive analyses, we conducted Pearson's r correlations and stepwise multiple regression analysis. The significance level for all tests was set at p<0.05.
3. Results
The descriptive statistics for all the psychosocial variables are displayed in table 2.
The results for the three dimensions of burnout show the following: the mean level of current professional efficacy is 4.25 (SD=1.51), burnout is 2.24 (SD=1.43), and cynicism is 1.32 (SD=1.19). For well-being/job satisfaction, the basic well-being scale, which combines the affect, competence, and expectations scales, showed a mean score of 161.64 (SD=25.83). In contrast, the collateral effects scale, which includes somatization, burnout, and alienation, has a mean of 19.54 (SD=7.48), notably lower than the basic well-being scale.
Regarding engagement, the dimensions of vigor (22.19), absorption (20.56), and dedication (22.96) show moderate-to-high values, with results being broadly similar across these dimensions. Finally, the mean score for occupational health is 114.43 (SD=20.44), indicating a moderately low presence of risk factors in the sample studied.
Next,
Table 3 presents the correlations between all the psychosocial variables studied and well-being/job satisfaction. It is evident that basic well-being is inversely correlated with cynicism and burnout and directly correlated with occupational health, indicating the absence of risk factors. Additionally, the collateral effects scale is inversely correlated with engagement, while the dimensions of engagement (vigor, dedication, and absorption) are directly correlated. However, it is important to note that the collateral effects scale does not correlate with one dimension of burnout—professional competence. The remaining significant correlations are detailed in
Table 3.
Finally, we analyzed the impact of the studied psychosocial variables on occupational well-being using stepwise multiple linear regression. The results showed that basic well-being was inversely predicted by cynicism (marginally significant) and burnout, while the absence of control and lack of support were direct predictors of basic well-being (see
Table 4).
4. Discussion and Conclusion
The general objective of this research was to assess the levels of burnout, occupational health (presence or absence of psychosocial risk factors), engagement, and well-being/job satisfaction in a sample of working adults with various sociodemographic characteristics. It was hypothesized that burnout levels would be high while well-being, health, and engagement levels would be moderate. However, our results do not support the initial hypothesis. In our sample, burnout levels were not high, while levels of occupational health, absence of psychosocial risks, engagement, and well-being/job satisfaction were moderately high. This suggests that the current changes in work models are not having a significant negative impact on workers' well-being.
We believe this could be due to the current “business-as-usual” situation, as organizations and employees have had time to adapt to new circumstances. They have also developed strategies to address risk factors introduced by the COVID-19 pandemic, such as hybrid and remote working models (Maung et al., 2023). Hybrid work, for instance, offers flexibility and improves work-life balance, helping to reduce stress and burnout (Krajčík et al., 2023). Additionally, many organizations have begun implementing psychological support measures and fostering more collaborative work environments, further contributing to employee well-being (Leka & Nicholson, 2019). Research also indicates that the experiences gained during the pandemic have strengthened workers' resilience in facing workplace challenges. By managing stress and communicating effectively, employees are now better equipped to handle psychosocial risks in their work environments (Gemine et al., 2021; Lubbadeh , 2020).
We also aimed to determine whether there was a relationship between the various variables studied. Our hypothesis posited that there would be a relationship among all the variables, according to our expectations: opposing variables would have an inverse relationship, while similar variables would show a positive correlation. Specifically, we expected that higher levels of occupational health and well-being/job satisfaction would correspond to lower levels of burnout and higher levels of engagement, and vice versa. Our findings support this hypothesis. Most variables and their dimensions correlate with one another, as expected. These findings align with those of Maslach and Leiter (2016), who describe an inverse relationship between burnout and engagement, existing on a continuum of workplace well-being. Burnout represents the negative end, while engagement represents the positive end. This continuum was confirmed in our study, with burnout dimensions—such as exhaustion and cynicism—negatively correlating with engagement and its dimensions. Moreover, prior studies have confirmed that burnout dimensions, such as exhaustion and cynicism, are negatively related to the components of engagement, such as dedication and vigor (Schaufeli & Bakker, 2004; Salanova et al., 2010; Lubbadeh , 2020). This inverse relationship suggests that excessive job demands without sufficient resources may increase burnout, decreasing engagement (Bakker & Demerouti, 2017).
Additionally, the relationship between burnout and occupational health can be observed through the associations between cynicism, exhaustion, and professional competence. Higher levels of cynicism and exhaustion correspond to lower levels of occupational health, defined as the absence of psychosocial risk factors. Conversely, higher levels of professional competence are associated with better occupational health, highlighting the relationship between burnout and occupational health. Specifically, increased cynicism and burnout among employees result in diminished occupational health, understood as the absence of psychosocial risk factors (Leka & Nicholson, 2019; Jain et al., 2021). Cynicism, characterized by a negative attitude toward work and the organization, fosters a toxic work environment that amplifies stress and lowers job satisfaction (Leiter & Maslach, 2024). Conversely, when employees feel competent in their roles, they report lower levels of burnout and cynicism, leading to improved occupational health (Döbler et al., 2022; Lubbadeh , 2020). These dynamics underscore the importance of investing in skills development and resource allocation to enhance employee performance and promote overall well-being.
Greater well-being at work is strongly associated with better occupational health, confirming a positive relationship between the two variables. Higher levels of well-being predict higher levels of occupational health and vice versa. In contrast, occupational health correlates negatively with the collateral effects scale, indicating that when occupational health decreases, negative effects may spill over into other areas of life. Various studies have substantiated this relationship, emphasizing the importance of creating work environments that foster job satisfaction and promote employees' physical and mental health (Demerouti et al., 2014; Sinclair et al., 2024). For instance, research by Bakker and Demerouti (2017) found that employees who experience high job satisfaction report fewer symptoms of burnout and greater engagement with their work. This supports the notion that higher baseline well-being is closely associated with better occupational health. Similarly, findings by Jain et al. (2021) suggest that job satisfaction not only enhances physical and mental health but also reduces the likelihood of experiencing negative collateral effects such as stress and burnout. Conversely, when well-being is low, there is a notable increase in negative outcomes, such as heightened stress, anxiety, and burnout. These adverse effects ultimately impair employees' overall health and job performance (Döbler et al., 2022; Martínez et al., 2023; Sonnentag & Fritz, 2015).
Regarding the impact of psychosocial variables on well-being, the two negative dimensions of burnout—cynicism and exhaustion—are shown to have a detrimental effect on workplace well-being. As levels of cynicism and exhaustion rise, a negative correlation with work well-being becomes evident, meaning employees experience reduced satisfaction and health in their work environment (Leiter & Maslach, 2024; Sinclair et al., 2024). On the other hand, engagement acts as a protective factor, along with social support and control. Research indicates that engagement, combined with social support and control, can help mitigate the adverse effects of burnout (Bakker & Demerouti, 2017; Shahwan et al., 2024; Sonnentag & Fritz, 2015). Social support provides employees with a network of resources to navigate stressful situations, while control refers to employees' ability to influence their work environment. These factors foster a sense of autonomy and capability, enhancing workplace well-being (Döbler et al., 2022; Jain et al., 2021).
Despite the results obtained, this study has certain limitations. The sample size (112 participants) is relatively small and should be expanded in future research to ensure more robust findings. Efforts should also be made to balance the number of participants across the various sociodemographic, personal, and work-related variables studied. Additionally, data collection relied on self-reporting via online surveys. While practical, this approach may have excluded workers with limited access to technology or those with less technological knowledge. Incorporating alternative techniques in future studies could allow for broader participation and provide a more accurate assessment of participants' working conditions.
In summary, our findings show that burnout — particularly the dimensions of exhaustion and cynicism — negatively impacts employee well-being. As these symptoms intensify, engagement decreases, emphasizing the importance of promoting a healthy work environment to improve employee engagement. Psychosocial factors, such as inadequate social support and lack of control, if poorly managed, can exacerbate the negative effects of burnout. Conversely, fostering a supportive and communicative workplace can mitigate these effects and improve occupational health. In this regard, engagement emerges as a critical protective factor against burnout. Engaged employees report higher levels of satisfaction and well-being, and professional efficacy is positively associated with occupational health. These findings suggest that developing the coping skills of employees and providing necessary resources are key to promoting workplace well-being. Therefore, organizations should implement strategies that enhance well-being, such as social support initiatives and programs that increase employee autonomy and control. Such measures could reduce burnout, boost engagement, and improve occupational health. Moreover, organizations should consider these relationships to create healthier and more productive work environments. For instance, it may be possible to enhance employee well-being and prevent burnout by managing psychosocial factors and encouraging engagement. Finally, implementing organizational strategies focused on employee well-being not only benefits employees but also positively impacts productivity and the workplace culture.
Author Contributions
R.L.M.: conceptualization and methodology; writing—original draft; formal analysis; writing—review and editing. A.I.O.B.: conceptualization, methodology, writing—review and editing. N.G.R.: writing—review and editing. M.L.V.: conceptualization and methodology; writing—review and editing.
Funding
This research received no external funding.
Institutional Review Board Statement
This research was conducted in accordance with the principles of the Declaration of Helsinki but does not involve medical experimentation, so it was not subject to approval by a local bioethics committee.
Informed Consent Statement
Informed consent was obtained from all individual participants included in the study.
Data Availability Statement
The data that support the findings of this study are not publicly available. The data are, however, available from the authors upon reasonable request.
Acknowledgments
We are grateful to the participants and collaborators who made this study possible.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Bakker, A.B.; Demerouti, E. Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology 2017, 22(3), 273–285. [Google Scholar] [CrossRef] [PubMed]
- Blanch, J. M., Sahagún, M., Cantera, L., Cervantes, G. Cuestionario de bienestar laboral general: estructura y propiedades psicométricas. Revista de Psicología del Trabajo y de las Organizaciones. 2010; 26, 2, 157–170. [CrossRef]
- Costin, A.; Roman, A.F.; Balica, R.S. Remote work burnout, professional job stress, and employee emotional exhaustion during the COVID-19 pandemic. Frontiers Psychology. 2023; 14, 1193854. [Google Scholar] [CrossRef]
- Demerouti, E., Bakker, A. B., Nachreiner, F., Schaufeli, W. B. (2019). The job demands-resources model
of burnout. Journal of Applied Psychology, 86(3), 499-512. [CrossRef]
- Demerouti, E., Bakker, A. B., Leiter, M. P. (2014). Burnout and engagement: A comprehensive model.
Annual Review of Organizational Psychology and Organizational Behavior, 7(1), 179-204. [CrossRef]
- Döbler, A.-S., Emmermacher, A., Richter-Killenberg, S., Nowak, J., Wegge, J. New insights into self-initiated work design: the role of job crafting, self-undermining and five types of job satisfaction for employee’s health and work ability. German Journal of Human Resource Management 2022, 36(2), 113–147. [CrossRef]
- Eurofound. (2023). Psychosocial risks and work-related stress in the context of teleworking. European Foundation for the Improvement of Living and Working Conditions.
- Krajčík, M., Schmidt, D. A., & Baráth, M. (2023). Hybrid Work Model: An Approach to Work–Life Flexibility in a Changing Environment. Administrative Sciences, 13(6), 150. [CrossRef]
- Khalkhali, M., Afzali, S., Nasiri, H. The relationship between occupational burnout and negative affective responses of nurses during the public health crisis. Journal of Nursing and Midwifery Sciences 2024, 11(1), e143199. [CrossRef]
- Koutsimani, P., Montgomery, A., & Georganta, K. (2023). The relationship between burnout, depression, and anxiety: A systematic review and meta-analysis. Frontiers in Psychology, 10, 284. [CrossRef]
- Leka, S., & Nicholson, P. J. (2019). Mental health in the workplace. Occupational Medicine, 69(1), 5–6. [CrossRef]
- Jain, A., Hassard, J., Leka, S., Di Tecco, C., & Iavicoli, S. (2021). The Role of Occupational Health Services in Psychosocial Risk Management and the Promotion of Mental Health and Well-Being at Work. International journal of environmental research and public health, 18(7), 3632. [CrossRef]
- Leiter, M. P., & Maslach, C. (2024). Job burnout. In L. E. Tetrick, G. G. Fisher, M. T. Ford, & J. C. Quick (Eds.), Handbook of occupational health psychology (3rd ed., pp. 291–307). American Psychological Association. [CrossRef]
- Lubbadeh, T. (2020). Job Burnout: A General Literature Review. International Review of Management and Marketing, 10(3), 7–15. [CrossRef]
- Martínez-Díaz A., Díaz-Fúnez, P. A., Salvador-Ferrer, C. M., Hernández-Sánchez, B. R., Sánchez-García, J. C., & Mañas-Rodríguez, M. (2023). Mediating effect of job crafting dimensions on influence of burnout at self-efficacy and performance: revisiting health-impairment process of JD-R theory in public administration. Frontiers Psychology, 14,1137012. [CrossRef]
- Maslach, C., & Leiter, M. P. (2016). Understanding the burnout experience: Recent research and its implications for psychiatry. World Psychiatry, 15(2), 103–111. [CrossRef]
- Maung, K. W., Soto-Rubio, A., & Bakker, A. B. (2023). Psychosocial factors influencing work engagement and burnout in hybrid work settings: A longitudinal analysis. Journal of Occupational and Environmental Medicine, 65(2), 135-145. [CrossRef]
- Parker, G., & Tavella, G. (2021). Distinguishing burnout from clinical depression: A theoretical differentiation template. Journal of affective disorders, 281, 168–173. [CrossRef]
- Passos, A. M., Souza, M. T., & Almeida, R. P. (2023). El impacto del burnout en la salud mental: La dificultad en la distinción de los síntomas con otros trastornos psicológicos. Psicología y Salud, 35(1), 45-59.
- Salanova, M., Agut, S., Peiró, J. M. Linking organizational resources and work engagement to employee performance and customer outcomes. The Journal of Applied Psychology, 2010; 90 (6), 1217–1227. [CrossRef]
- Shahwan, S., Tay, E. H., Shafie, S., Tan, Y. B., Gunasekaran, S., Tan, R. H. S., Satghare, P., Zhang, Y., Wang, P., Tan, S. C., & Subramaniam, M (2024). The protective role of resilience and social support against burnout during the COVID-19 pandemic. Frontiers. Public Health. 12, 1374484. [CrossRef]
- Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. Journal of Organizational Behavior, 25(3), 293-315. [CrossRef]
- Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 3(1), 71–92. [CrossRef]
- Sinclair, R. R., Britt, T. W., & Watson, G. P. (2024). Psychological well-being and occupational health: Caught in the quicksand or standing on a firm foundation? In L. E. Tetrick, G. G. Fisher, M. T. Ford, & J. C. Quick (Eds.), Handbook of occupational health psychology (3rd ed., pp. 385–403). American Psychological Association. [CrossRef]
- Sonnentag, S.; Fritz, C. Recovery from job stress: The role of psychological detachment and mindfulness. Journal of Occupational Health Psychology 2015, 27(4), 237–250. [Google Scholar] [CrossRef]
- Soto-Rubio, A., Giménez-Espert, M. D. C., Prado-Gascó, V. (2020). Effect of Emotional Intelligence and
Psychosocial Risks on Burnout, Job Satisfaction, and Nurses' Health during the COVID-19 Pandemic.
International Journal Of Environmental Research And Public Health, 17(21), 7998. [CrossRef]
- Van Zoonen, W., & Sivunen, A. E. (2021). The impact of remote work and mediated communication frequency on isolation and psychological distress. European Journal of Work and Organizational Psychology, 31(4), 610–621. [CrossRef]
Table 1.
Sociodemographic data of the sample.
Table 1.
Sociodemographic data of the sample.
| VARIABLE |
N |
% |
| Gender |
Man |
49 |
43.7 |
| Woman |
63 |
56.3 |
| Age |
18 to 20 years |
8 |
7.2 |
| 21 to 30 years old |
41 |
36.6 |
| 31 to 40 years old |
14 |
12.5 |
| 41 to 50 years |
22 |
19.6 |
| More than 50 years |
27 |
24.1 |
| Marital status |
Single |
31 |
27.7 |
| Married |
48 |
42.9 |
| Divorced |
8 |
7.1 |
| Widowed |
0 |
0 |
| Stable couple |
25 |
22.3 |
| Income level (monthly) |
Less than €1000 |
37 |
33 |
| Between 1000€ and 2000€. |
54 |
48.2 |
| Between 2000€ and 3000€. |
19 |
17 |
| More than 3000€. |
2 |
1.8 |
| Workday |
1-10 hours per week |
11 |
9.8 |
| 11-20 hours per week |
15 |
13.4 |
| 21-30 hours per week |
15 |
13.4 |
| 31-40 hours per week |
71 |
63.4 |
| Educational level |
No education |
2 |
1.8 |
| Primary |
8 |
7.1 |
| Secondary |
47 |
42 |
| University students |
39 |
34.8 |
| Master's/Doctorate |
16 |
14.3 |
| Company Type |
Public |
45 |
40.2 |
| Private |
62 |
55.4 |
| Mixed |
5 |
4.4 |
| Size of company (no. of employees) |
Microenterprise |
23 |
20.5 |
| Small business |
19 |
17 |
| Medium-sized company |
47 |
42 |
| Large company |
23 |
20.5 |
| Sector |
Primary |
2 |
1.8 |
| Secondary |
8 |
7.1 |
| Tertiary |
102 |
91.1 |
Table 2.
Descriptive results for all psychosocial variables measured.
Table 2.
Descriptive results for all psychosocial variables measured.
| VARIABLES |
Min |
Max |
Mean |
SD |
| Professional competence |
0 |
7 |
4.25 |
1.51 |
| Exhaustion |
0 |
7 |
2.24 |
1.43 |
| Cynicism |
0 |
7 |
1.32 |
1.19 |
| Affect Scale |
10 |
70 |
37.41 |
6.70 |
| Competency Scale |
10 |
70 |
40.14 |
7.44 |
| Expectations Scale |
21 |
147 |
94.09 |
13.47 |
| Somatization Scale |
1 |
7 |
2.00 |
1.23 |
| Burnout Scale |
3 |
15 |
8.28 |
3.32 |
| Alienation Scale |
4 |
28 |
9.27 |
3.90 |
| Well-being |
41 |
287 |
161.64 |
25.83 |
| Collateral Effects |
8 |
56 |
19.54 |
7.48 |
| Vigor |
0 |
30 |
22.19 |
6.56 |
| Absorption |
0 |
36 |
20.56 |
6.67 |
| Dedication |
0 |
30 |
22.96 |
9.27 |
| Engagement |
0 |
96 |
65.71 |
19.01 |
| Occupational health |
40 |
200 |
114.43 |
20.44 |
| Control |
8 |
40 |
23.19 |
5.85 |
| Support |
12 |
60 |
41.95 |
7.92 |
| Rewards |
11 |
55 |
21.12 |
7.02 |
| Cognitive Demands |
9 |
45 |
21.04 |
7.97 |
Table 3.
Correlations between the measured psychosocial variables (p<0.05*; p<0.01**).
Table 3.
Correlations between the measured psychosocial variables (p<0.05*; p<0.01**).
| |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
| 1 Efficacy |
- |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 Exhaustion |
0.35** |
- |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3 Cynicism |
0.25** |
0.63** |
- |
|
|
|
|
|
|
|
|
|
|
|
|
| 4 Affect |
|
-0.47** |
-0.50** |
- |
|
|
|
|
|
|
|
|
|
|
|
| 5.Competencies |
|
-0.38** |
-0.41** |
0.81** |
- |
|
|
|
|
|
|
|
|
|
|
| 6 Expectations |
|
-0.45** |
-0.46** |
0.77** |
0.83** |
- |
|
|
|
|
|
|
|
|
|
| 7 Burnout |
|
0.63** |
0.39** |
-0.35** |
-0.27** |
-0.33** |
- |
|
|
|
|
|
|
|
|
| 8 Alienation |
|
0.51** |
0.45** |
-0.45** |
-0.42** |
-0.44** |
0.72** |
- |
|
|
|
|
|
|
|
| 9 Somatization |
-0.25** |
0.25** |
0.20* |
|
-0.22* |
|
0.59** |
0.48** |
- |
|
|
|
|
|
|
| 10 Basic Wellbeing. |
|
-0.47** |
-0.49** |
0.89** |
0.93** |
0.96** |
-0.34** |
-0.47** |
-0.19* |
- |
|
|
|
|
|
| 11 Collat-effects |
|
0.59** |
0.44** |
-0.42** |
-0.37** |
-0.40** |
0.92** |
0.92** |
0.68** |
-0.43** |
- |
|
|
|
|
| 12 Vigor |
0.31** |
|
-0.25** |
0.34** |
-0.35** |
0.44** |
|
|
|
0.42** |
|
- |
|
|
|
| 13 Absorption |
0.24* |
|
|
|
0.22* |
0.29** |
|
|
|
0.25** |
|
0.74** |
- |
|
|
| 14 Dedication |
0.42** |
|
-0.20* |
0.34** |
0.36** |
0.47** |
|
|
|
0.44** |
|
0.88** |
0.75** |
- |
|
| 15 Occup-Health . |
|
-0.20* |
-0.26** |
0.20* |
0.28** |
0.34** |
|
-0.21* |
|
0.31** |
-0.21* |
|
|
|
- |
| 16 Control |
|
-0.29** |
-0.26** |
0.34** |
0.40** |
0.41** |
-0.30** |
-0.42** |
-0.22** |
0.42** |
-0.39** |
|
|
|
0.76** |
| 17 Support |
0.30** |
|
-0.30** |
0.35** |
0.40** |
0.47** |
-0.25** |
-0.39** |
-0.21** |
0.45** |
-0.35** |
0.24* |
0.26** |
0.24* |
0.81** |
| 18 Rewards |
|
-0.27** |
-0.24** |
0.20* |
0.30** |
0.32** |
-0.29** |
-0.26** |
-0.24** |
0.30** |
-0.30** |
|
|
|
0.71** |
| 19 Cog-demands |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
0.38** |
Table 4.
Significant predictors of well-being at work.
Table 4.
Significant predictors of well-being at work.
| |
Predictors |
R 2cor. |
β stand. |
t |
p |
Basic Wellbeing (F=19.558, p=0.000**)
|
Cynicism |
0.234 |
-0.173 |
-1.837 |
0.069 |
| DECORE Support |
0.329 |
0.197 |
2.244 |
0.027* |
| Engagement |
0.394 |
0.293 |
4.040 |
0.000** |
| Exhaustion |
0.439 |
-0.246 |
-2.666 |
0.000* |
| DECORE_Control |
0.455 |
0.176 |
2.035 |
0.044* |
|
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).