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Article

Organizational and Individual Factors Influencing the Quality of Working Life Among Brazilian University Professors during COVID-19

by
Vanessa Molinero de Paula
1,
Júlia Teles
2 and
Teresa Patrone Cotrim
3,4,*
1
Faculdade de Fisioterapia, Universidade de Rio Verde, Goiás 75901-970, Brazil
2
CIPER, Centro Interdisciplinar de Performance Humana, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, 1499-002 Cruz-Quebrada, Portugal
3
Laboratório de Ergonomia, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, 1499-002 Cruz-Quebrada, Portugal
4
CIAUD, Centro de Investigação em Arquitetura, Urbanismo e Design, Faculdade de Arquitetura, Universidade de Lisboa, Rua Sá Nogueira, Pólo Universitário do Alto da Ajuda, 1349-055 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(15), 6351; https://doi.org/10.3390/su16156351
Submission received: 13 June 2024 / Revised: 17 July 2024 / Accepted: 22 July 2024 / Published: 25 July 2024
(This article belongs to the Special Issue Sustainable, Safe and Healthy Workplaces after COVID-19: Lessons Learned for the Future of Work in the Post-pandemic Era)
Versions Notes

Abstract

:
The COVID-19 pandemic suddenly impacted professors’ work demands and their adaptation to new technologies and work organization, namely, working from home. These changes urged us to know its impact on their quality of working life (QWL). This study aimed at characterizing the main determinants of the quality of working life in Brazilian university professors during the COVID-19 pandemic. It integrated two cross-sectional analyses, through a questionnaire applied in 2020–2021 and 2021–2022, at universities in central-western Brazil. The sample included 220 professors at the first moment and 180 at the second. The main tool used was the Quality of Working Life Questionnaire. Multiple linear regression analyses were performed to identify the significant predictors of the QWL. In 2020–2021, “Sleep quality” was the main predictor with a 15.8% contribution to explaining the QWL variability, followed by “Satisfaction with supervisors”, “Depression”, “Work–family conflict” and “Difficulties with materials or physical resources”. In 2021–2022, the predictors that contributed most to explain the QWL variability were “Work–family conflict” and “Sleep quality” with contributions of 13.9% and 12.2%, respectively, to the coefficient of determination. “Satisfaction with students”, “Difficulties in interpersonal relationships with supervisors”, “Noise”, “Smoking habits” and “Biological sex” had lower contributions. The knowledge of the predictors of QWL among university professors is essential to defining strategies to prevent occupational risks and promoting workers’ health and sustainable quality of working life.

1. Introduction

The concept of quality of working life (QWL) continues to be extremely important for the labour force, and it is still relevant today, given its multidimensionality and holistic perspective [1]. Although its definitions are not consensual [2], and somewhat lacking precision because of it being a dynamic construct and subjective notion [3], most of them include the environmental, physical, organizational, psychosocial and individual dimensions of work, with the aim of achieving workers’ health and well-being [2]. Another important perspective is the impact of QWL on other dimensions, such as lower turnover, organizational performance and productivity, from the point of view of companies [2,4], or work engagement, job satisfaction and well-being, from the point of view of individuals [2]. From another standpoint, the spillover model explains the relations between quality of life and QWL, so that the positive correlations between work and private life allow the justification of its impact on well-being [3].
In summary, QWL integrates individual, social, organizational and environmental factors [2,3] that can be grouped in different manners or assessed by different tools. Among the various dimensions that influence quality of working life, it is recognized that the work environment, the content of the work, and the social support of supervisors, managers or colleagues are relevant and affect the perception of safety and health at work [2]. But Martel & Dupuis [3] (p. 355) defined QWL as “a condition experienced by the individual in his or her dynamic pursuit of his or her hierarchically organized goals within work domains where the reduction of the gap separating the individual from these goals is reflected by a positive impact on the individual’s general quality of life, organizational performance, and consequently the overall functioning of society”. This definition reinforces the notion that the concept of QWL is subjective and depends on the individual perception at a certain moment. From this perspective, other authors believe that it is crucial for workers to define long-term objectives. This not only helps them get involved in more interesting tasks but also aids in learning processes, reinforcing their competencies and earning recognition [5]. In other words, despite the limitations of the definitions and the different lists of the types of dimensions that make it up, this concept is essential in defining programs to improve their QWL, and four essential dimensions can be considered as the following: the task, and the physical, psychosocial and organizational contexts of work [3]. Naturally, these dimensions are related to working conditions and safety at work, balancing work and family life, working time organization, access to lifelong learning and training and career progression, as well as the possibility of participating in processes to improve workplaces [5]. These dimensions were also analysed in other equally complex constructs, such as job quality, used by Eurofound [6], and considered crucial to work sustainability as people age. Human and social capital are essential pillars of work sustainability, and they refer also to individuals’ health and motivation to contribute to work systems [7], therefore contributing to a sustainable quality of working life.
One of the primary challenges in researching QWL is the variation in dimensions and indicators used by different authors [1,2,3,4,5]. Additionally, there is a disparity in the instruments employed for assessing either quality of life [8] or quality of working life [1,9], despite the related nature of these concepts. As a result, these measures do not capture the same construct, making it difficult to make direct comparisons of results, even when similar trends are observed across various studies.
Regarding its application to different work sectors, university professors and teachers from different educational levels have already been studied [9,10,11,12], but there is still a lack of knowledge of what the main determinants of QWL in university professors are, and in particular, during the COVID-19 pandemic, when the imposition of compulsory teleworking and new ways of organizing work may have influenced the perception of QWL. In some recent studies, the effect of technostress on the quality of life at work of university professors was studied and found to be negative. In other words, the increased workload and time pressure resulting from the intensive use of new technologies, particularly during COVID-19, had a negative impact on teachers’ QWL [11]. Some other studies involving secondary school teachers have highlighted the significance of perceived organizational support in improving their QWL. These studies have shown that when teachers can be involved in decision-making processes, this contributes to a better sense of their social relevance within the organization, provides opportunities for personal and professional growth, and subsequently enhances their overall QWL [12]. The perception of good QWL greatly enhances personal and organizational effectiveness [13], contributing to more sustainable career paths among professors.
In addition, there have been organizational changes that have continued since the COVID-19 pandemic, such as greater openness to hybrid teleworking arrangements. Understanding the determinants of QWL contributes to retaining these skilled workers and making their working lives sustainable.
Thus, the main objective of this study was to characterize the main determinants of QWL in Brazilian university professors during the COVID-19 pandemic.
This paper analysed the relationships between individual, environmental, psychosocial, and health factors and QWL to understand its determinants.

2. Materials and Methods

This study integrated two cross-sectional analyses, with data collection carried out through an online questionnaire applied at two points in time, November to January 2020–2021 and 2021–2022, during the COVID-19 pandemic, at universities in central-western Brazil. It was not possible to carry out a longitudinal study, as the questionnaires were anonymous and did not comprise information that would allow the answers to be coded and paired at the two data collection points.

2.1. Participants

The study population comprised 508 university professors from two public universities in the state of Goiás, Brazil, the University of Rio Verde and the Goiano Federal Institute.
The sample size was calculated using GPower 3.1 [14]. Considering the main objective of this study, an a priori power analysis to determine the sample size to test the significance of the multiple linear regression model (fixed effects model) was carried out. The results indicated that a sample size of 155 individuals was needed to detect medium effect sizes (f2 = 0.2), with a power of 80 per cent (1 − β = 0.80), at the usual significance level of α = 0.05.
The sample included 220 professors at the first moment of data collection and 180 at the second, representing response rates of 43.3% and 35.4%, respectively. The inclusion criteria included being a professor at the selected universities, accepting informed consent and having teaching duties. All questionnaires in which the scale assessing quality of working life was not completely filled in and did not allow it to be quoted were excluded.

2.2. Variables

The dependent variable was the quality of working life. The independent variables were grouped as individual (age, biological sex, number of dependents, such as children or relatives in the ascending line, smoking, physical exercise, coffee intake, sleep quality, stress, anxiety and depressive symptoms, absenteeism, type of teleworking), related to the physical environment (level of difficulty when interacting with equipment, discomfort with noise, lighting, temperature) and related to the psychosocial conditions (satisfaction with supervisors, students and colleagues, work–family conflict), in order to reflect the main dimensions expressed in the literature review.

2.3. Data Collection Tools

The first part of the questionnaire assessed individual and lifestyle variables and the variables related to the physical environment based on other studies in which they were assessed [15].
The assessment of the perception of depression, stress and anxiety was performed using the Brazilian version of the DASS-21 questionnaire [16]. This scale consists of 21 questions corresponding to three sub-scales of seven items designed to assess depression, anxiety and stress. The scales range from 0 to 42 points, where the highest value corresponds to the worst results.
The musculoskeletal symptoms in the last 12 months were assessed using the Brazilian version of the Nordic Questionnaire [17,18].
Sleep quality was evaluated using the Brazilian version of the Pittsburgh questionnaire with 19 self-evaluative items and five directed to a roommate [19,20]. The highest value corresponds to a very poor sleep quality.
The work–family conflict was assessed using the scale included in the Brazilian version of the Copenhagen Psychosocial Questionnaire [21]. The highest values correspond to the worst perception of work–family conflict.
The final part of the questionnaire comprised the Brazilian version of the Quality of Working Life Questionnaire (QWLQ-bref) [22] which measures the quality of life at work. This is an abbreviated version of the QWLQ-78, with 20 questions concerning four domains: “Physical/Health”, which has four questions regarding health, diseases, work and habits of workers; “Psychological”, which has three questions concerning personal satisfaction, motivation at work and workers’ self-esteem; “Personal”, which has four questions about family themes, personal beliefs, culture and the way they influence work; and the “Professional” domain, which has nine questions regarding organizational factors such as freedom, participation, responsibility, equal treatment, pride in the organization, training, variety of tasks, spirit of camaraderie and satisfaction [22]. The highest values correspond to the best perception of QWL.

2.4. Statistical Analysis

Descriptive analysis was carried out presenting the frequency distributions of responses to qualitative variables and statistical measures, such as the mean, standard deviation (SD), and minimum and maximum values for quantitative variables.
Normality was assessed using the Shapiro–Wilk test. Whenever the normality assumption was not validated, non-parametric tests were used. Independent-sample t-tests, ANOVA tests and Kruskal–Wallis tests were performed to compare the quality of working life between the categories of some independent variables. Pearson’s correlation coefficient (R) was used to assess the association between some of the independent variables among themselves and between some of the independent variables and quality of working life.
Multiple linear regression analyses [23] using the backward stepwise method, were conducted to identify the significant predictors of the quality of working life in the two data collection periods, 2020–2021 and 2021–2022. The set of candidate predictors considered for multiple linear regression analysis was established using the criteria of p < 0.25 in a priori bivariate analysis. For the final models, the normality of residuals was verified using the Shapiro–Wilk test and graphical residual analyses were also conducted to ensure no violation of the assumptions of homoscedasticity and linearity. The contribution of each predictor (R2 cont.) to the overall R-squared of the model (R2) was calculated as the product of the predictor’s standardized coefficient and the correlation between the predictor and the dependent variable. The variance inflation factor (VIF) was used to verify that there was no multicollinearity: a VIF < 5 indicated that the variable had low multicollinearity, 5 ≤ VIF < 10 indicated moderate multicollinearity and VIF ≥ 10 suggested high multicollinearity. All statistical analyses were performed with the software SPSS v.29 [24] and a significance level of 5% was used.

2.5. Ethical Procedures

This study was approved by the ethics committee of the University of Rio Verde (Code Number: 3.745.316) and of the Human Kinetics Faculty of the University of Lisbon (Code Number: 20/2020), respecting the guidelines of the Declaration of Helsinki [25]. Informed consent was obtained from all the participants before the start of the research. Participation was voluntary and anonymous, and participants were free to leave the questionnaire at any time.

3. Results

The quality of working life among our sample of professors in 2020–2021 had an average score of 3.91 [±0.61] ranging from 1.74 to 5.00, with a median of 3.94 points, and in 2021–2022, the mean score was 3.17 [±0.51], with a minimum value of 1.76, a maximum of 5.00 and a median of 3.00 points. The perception of the quality of working life was lower in 2021–2022 and the differences were statistically significant (t = 12.263; p < 0.001).

3.1. Individual Factors and Quality of Working Life

This section assessed the characteristics of sociodemographic and lifestyles variables and the associations between these variables and QWL (Table 1).
Age and seniority in teaching correlated with QWL only at the second data collection moment (2021–2022) (Table 1). The correlations were positive, although weak, which points to a trend toward a better perception of quality of working life as age and seniority increases.
The body mass index (BMI) had no association with QWL in both moments. But absenteeism correlated negatively with QWL in 2021–2022 (Table 1), meaning that those who miss work due to illness have a worse perception of their quality of working life.
Statistically significant differences were found in the perception of QWL between females and males only in the first data collection moment (2020–2021), with better results in the latter group. Statistically significant differences were additionally found in the perception of QWL according to smoking habits in 2021–2022, with better results in the non-smoking group. In the same period, the type of teleworking also influenced the perception of QWL, with differences between the groups that worked remotely and in person, with the latter showing better results (Table 2).

3.2. Environmental Factors and Quality of Working Life

The perception of quality of life at work differed according to the level of difficulty with material and physical resources (only in 2020–2021) and with technological resources (at both moments), with the groups who considered they had no difficulties showing a better perception of their QWL. The results also differed according to the perception of discomfort with the environmental conditions of noise, lighting and temperature in the summer. Those with higher levels of comfort also perceived better levels of QWL (Table 3).

3.3. Psychosocial and Health-Related Factors and Quality of Working Life

The level of satisfaction with colleagues, students and supervisors also influenced the perception of QWL. Those with better satisfaction levels perceived a better QWL in both data collection moments. Regarding musculoskeletal symptoms in the cervical and lower back regions during the last 12 months, those without symptoms perceived a better QWL in both moments (Table 4).
In 2020–2021, the perceptions of work–family conflict, stress, anxiety, depression and sleep disturbances correlated positively between themselves, meaning that an increase in each contributed to an increase in all the others, that is, a more negative result in each contributed to a more negative result in all the others. In 2021–2022, only anxiety, stress and depression were positively highly correlated. Work–family conflict was positively correlated with sleep disturbances (Table 5).
In 2020–2021, the perception of work–family conflict, stress, anxiety, depression and sleep disturbances correlated negatively with the QWL, meaning that those with these problems perceived a lower QWL. In 2021–2022, only the perception of work–family conflict and sleep disturbances showed a negative correlation with the QWL (Table 6).

3.4. Predictors of the Quality of Working Life

Backward stepwise linear regression for the QWL in the period 2020–2021 (QWL2020–2021) allowed us to achieve a significant regression equation (F(7,195) = 27.958, p < 0.001) with five predictors: “Difficulties with materials or physical resources” (DiffMPR), “Satisfaction with supervisors” (SatSup), “Sleep quality” (Pittsburgh score), “Depression” and “Work–family conflict”. The regression model that was proposed to explain the QWL in the period 2020–2021 (results in Table 7) is in Equation (1):
QWL2020–2021 = 4.516 + 0.346 DiffMPR (Never) + 0.408 DiffMPR (Seldom) − 0.147 SatSup (Unsatisfied) − 0.470 SatSup (Neutral) − 0.045 Pittsburgh score − 0.018 Depression − 0.104 Work–family conflict
The coefficient of determination of this model is R2 = 0.501 and the adjusted R2 = 0.483. The “Sleep quality“ (Pittsburgh score) was the predictor that gave a bigger contribution, 15.8%, to explain the QWL variability in 2020–2021, followed by “Satisfaction with supervisors” and the “Depression” score with contributions of 12.5% and 11.5%, respectively. “Work–family conflict” and “Difficulties with materials or physical resources” did not have such high contributions: 5.5% and 4.8%, respectively (Table 7).
The backward stepwise linear regression for QWL in the period 2021–2022 (QWL2021–2022) reached a significant regression equation (F(11,145) = 14.287, p < 0.001) with seven predictors: “Biological sex” (BSex), “Difficulties in interpersonal relationship with supervisors” (DIRS), “Smoking habits” (Smoke), “Satisfaction with students” (SwS), “Noise”, “Sleep quality” (Pittsburgh score) and “Work–family conflict”. The regression model that was proposed to explain QWL in the period 2021–2022 (results in Table 8) is in Equation (2):
QWL2021–2022 = 3.128 + 0.148 BSex (Female) + 0.509 DIRS (Never) + 0.231 DIRS (Seldom) + 0.171 Smoke (Non-smoker) − 0.020 Smoke (Ex-smoker) − 0.243 SwS (Unsatisfied) − 0.246 SwS (Neutral) + 0.201 Noise (Comfortable) + 0.145 Noise (Moderate) − 0.036 Pittsburgh score − 0.196 Work–family conflict
The coefficient of determination of this model is R2 = 0.520 and the adjusted R2 = 0.484. The predictors that contributed most to explain the QWL variability in 2021–2022 were “Work–family conflict” and “Sleep quality” (Pittsburgh score) with contributions of, respectively, 13.9% and 12.2% to the coefficient of determination. “Satisfaction with students” had a contribution of 8.5% and “Difficulties in interpersonal relationships with supervisors” a contribution of 7%. “Noise”, “Smoking habits” and “Biological sex” had lower R2 contributions with values of 4.1%, 3.9% and 2.4%, respectively (Table 8).

4. Discussion

In today’s universities, the level of demand for the activity and performance of professors is increasing all over the world. Professors need to create, use and share knowledge with their students, peers and community [26]. This increase in professors’ work demands has consequences for their health, well-being and quality of life at work. In addition to these changes in the traditional roles of professors in universities, the COVID-19 pandemic had a sudden impact on their adaptation to new technologies and a new model of work organization that would allow professors to work from home, integrally at first and then in a hybrid regime [27].
New legislation and provisional measures have been taken, causing changes in university policies, giving rise to new collective and individual agreements in a short period of time. The pandemic has affected working relationships and brought countless uncertainties, which can have influenced the quality of life at work. So many changes have meant that universities have had to make quick decisions and seek solutions to issues, such as flexible working hours and workplaces, delegation of responsibilities, and less control over employees by their superiors [27,28].
One of the questions that these changes poses is what the impact on their quality of working life has been, as well as what were the main determinants of QWL during the COVID-19 pandemic. In accordance with the questions raised previously, this study aimed at characterizing the main determinants of QWL in Brazilian university professors during the COVID-19 pandemic and obtained interesting results that met the multidimensionality of the concept.
To frame the discussion of the results, it should be noted that the samples of the 2020–2021 and 2021–2022 periods were not related samples, despite being drawn from the same population, due to restrictions in coding the participants. This could be a limitation of this study when interpreting the results.
Regarding teleworking in both data collection moments, most of the sample was in a hybrid model, respectively, 51.4%, and 58.4% in 2020–2021 and 2021–2022. The hybrid model was mainly adopted by professors with practical classes in the areas of health sciences, for instance. In 2021–2022, the number of respondents working in-person was very low (5.4%), which may have influenced the results from the second data collection moment. This may have resulted from differences in the scientific areas of respondents, as they were not stratified in the same way in the two periods, being one the main limitations of this study.
In 2020–2021, the sample of professors were facing the first wave of COVID-19, and work-related measures, like working from home, had recently been implemented. Several studies have shown the advantages and disadvantages of adaptations, particularly of remote working. Some of the negative effects included an increased workload, longer working hours, difficulties with technological and digital systems, but also with the layout of spaces, and equipment, the influence of distance learning on relationships with students and colleagues, and uncertainty [28,29,30]. These negative changes in the work activity of university professors had an impact on health and well-being with an increase in stress, anxiety and musculoskeletal symptoms, among others [30,31].
The perception of the QWL was worse at the second moment of data collection (2021–2022), which may reflect the impact of longer exposure to the overload imposed by the organizational changes resulting from the COVID-19 pandemic but may also be the result of differences in the composition of the sample, particularly those relating to the lower percentage of professors working in person.
The regression model obtained for the period of 2020–2021 reflects the relevance of sleep disturbances, satisfaction with supervisors and depression as main predictors of the QWL, followed by work–family conflict and difficulties with materials and physical resources. The model for the period of 2021–2022 showed different predictors such as satisfaction with students, difficulties in interpersonal relationships with supervisors, noise, smoking habits and gender, but the main predictors were also present in the 2020–2021 model, being quality of sleep disturbances and work–family conflict.
During the COVID-19 pandemic, social isolation and changes in work organization, among other factors, led to an increase in sleep disturbances among working populations [32]. Several studies have shown that disturbances in sleep quality have a strong impact on individuals’ daytime activities, with repercussions on their QWL [33]. While one of its most common consequences is daytime sleepiness, other consequences are recognized, such as changes in cognitive functioning (attention and memory), higher levels of depression and anxiety, increased musculoskeletal symptoms, poor interpersonal relationships, decreased productivity, and lower occupational well-being [33,34,35]. In this way, it seems that there is an influence of disturbed sleep quality on quality of life at work, either directly or through this set of consequences in these individuals’ daily working lives. Although the present study was cross-sectional and therefore limited interpretations regarding changes in the resulting associations, disturbances in sleep quality correlated with anxiety, depression and stress in 2020–2021, but these associations were not found in 2021–2022. In this study, it was not possible to identify whether these results were due to differences in the sample or to the acute effect of the pandemic on professors’ levels of stress and anxiety at the first moment of data collection. To uncover these aspects, we would have needed to carry out a longitudinal study, which was not possible and is a limitation of this study.
In both models, interpersonal relationships with supervisors appeared as determinants of quality of life at work, either from a positive and supportive perspective, or from a negative perspective of difficulty in these relationships. Positive social relationships and social support from managers and supervisors in the workplace can have a protective effect on workers’ health and well-being and contribute to job satisfaction [36,37,38,39]. However, the lack of social support from supervisors can have a negative impact on health, contributing to depressive symptoms and anxiety [37]. Satisfaction with supervisors will depend on the type of social support they provide and the perception of interactional justice inherent in the support received by workers from those same managers. This perception of interactional justice can include receiving adequate feedback, respectful communication and explanations that are appropriate to the work situation [36]. Social support from superiors is a variable recognized as having a positive influence on workers’ health and well-being [37,38], but its relationship with quality of life at work has not been as well studied. Nevertheless, the influence of transformational leadership on workers’ motivation and commitment is recognized, which are factors enhancing an enabling environment and promoting QWL and work sustainability [40]. In this study, satisfaction with supervisors appeared to be a favourable and important determinant of quality of life at work, thus contributing to a sustainable QWL. In the opposite direction, difficulties in relationships with supervisors led to a poorer quality of life at work.
Still on interpersonal relationships in the work context, relationships with students can also play a relevant role in professors’ job satisfaction and health, particularly when the preferred communication channel changes from direct and in-person to distance and mediated by technology. Some studies have shown that professors’ job satisfaction is related to their positive perception of the quality of relations with students [41]. Again, lower levels of job satisfaction are related to poor health, and higher levels of stress [42] may influence the quality of working life. The teaching professions are known for the presence of high levels of stress, and part of the explanatory factors may be related to the management of classrooms and the interaction with students [41].
With the advent of mandatory teleworking among universities during COVID-19, one of the main consequences has been the blurring of the boundaries between work and family, with negative effects and the occurrence of the phenomenon of work–family conflict. Another factor that contributes to work–family conflict is the increase in the length of working hours or working during socialization periods such as late afternoons or evenings. It was already common for university lecturers to work late afternoon and early evening shifts, but during COVID-19, these shifts began to take place from home. The presence of work–family conflict is therefore understandable. Several studies have shown a reciprocal relationship between work–family conflict and emotional exhaustion [43] and its influence on mental health, subjective well-being [44] and lower job satisfaction [45], which in turn can influence quality of life and quality of life at work according to its definition [2]. Therefore, improving the balance between work and family will contribute to QWL’s sustainability.
Despite a smaller contribution to the 2021–2022 model, noise emerged as a predictor of quality of life at work. This result is interesting insofar as exposure to noise can have psychophysiological effects and influence levels of stress [46], but also because of the importance that should be given to environmental factors and their management in the workplace. Managing environmental factors becomes more difficult when working from home, but attention should be paid to their influence, as is the case with quality of life at work. This factor underscores the importance of preventing occupational risks and designing work systems to enhance the QWL sustainability.
Regarding gender, being a female appeared to contribute to a better perception of the quality of working life, but this result appeared only in the second data collection moment, and it is not in line with previous research that pointed to a better perception among men [7]. These results must be interpreted with caution given the cross-sectional nature of this study and the size of the sample.
Based on the results obtained, it was possible to identify priority areas for intervention for both public institutions studied, oriented to organizational, psychosocial, environmental and individual dimensions.
In the organizational and psychosocial domains, important factors include satisfaction with supervisors and students. These findings align with theoretical models suggesting that improving job satisfaction in various areas leads to a better quality of work life [1]. Universities can therefore implement strategies to foster positive social interactions among colleagues, as well as between professors and students, to meet social needs. Work–family conflict was also identified as a significant factor. This highlights the need for universities to address work–life balance by considering flexible scheduling to accommodate family needs. In the environmental domain, while noise and physical resources had minimal impact on the models, they can still be included in university policies to invest in preventing the exposure of staff and students to disruptive noise or inadequate physical environments which may negatively affect their satisfaction, health and safety. Lastly, the individual dimension pertains to health and well-being at work, which can be addressed through measures to promoting good health, such as improving sleep quality and reducing the risk of mental disorders.

5. Conclusions

The results of this study contributed to our knowledge of the predictors of quality of life at work among Brazilian university professors during the COVID-19 pandemic. The different determinants found in the two models may have been influenced by the differences in the constitution of the two samples. They also reflect the adaptation to the changes imposed due to the COVID-19 pandemic at the first moment, such as difficulties with material and physical resources and satisfaction with supervisors, and their evolution over a year until the second moment of data collection. The results from the first data collection reflect the abrupt introduction of working from home into these professors’ daily working lives.
This information is essential to defining strategies to prevent occupational risks and promote workers’ health, since factors such as satisfaction with supervisors, depression, sleep quality disorders and work–family conflict have emerged as important determinants of quality of life at work. The results meet the multidimensionality of the QWL concept and may suggest that measures to promote health and well-being at work should be directed primarily at organizational and interpersonal relationships and work–life balance. In this study, identifying the factors that positively and negatively influenced QWL during the COVID-19 pandemic contributed to a clearer definition of intervention priorities for sustainable QWL among university professors facing a demanding period.
The relevance of QWL in improving workers’ health and well-being should be noted, along with its role in increasing organizational performance, productivity and contributing to a sustainable work of university professors for the future.

Author Contributions

Conceptualization, V.M.d.P. and T.P.C.; methodology, V.M.d.P., J.T. and T.P.C. formal analysis, V.M.d.P., J.T. and T.P.C.; data collection, V.M.d.P.; data curation, V.M.d.P. and T.P.C.; writing—original draft preparation, V.M.d.P., J.T. and T.P.C.; writing—review and editing, J.T. and T.P.C.; supervision, T.P.C.; project administration, V.M.d.P. and T.P.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financed by Portuguese funds through the FCT—Fundação para a Ciência e a Tecnologia, I.P., under the Strategic Project with the Reference Numbers UIBD/04008/2020 and UIDP/04008/2020.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of University of Rio Verde (Protocol Code 3.745.316, 5 December 2019) and of the Human Kinetics Faculty of the University of Lisbon (Protocol Code 20/2020, 29 July 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

Data are unavailable due to privacy and ethical restrictions.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Table 1. Sociodemographic characteristics in 2020–2021 and 2021–2022, age, seniority in teaching, BMI and absenteeism, and correlation with quality of working life (QWL).
Table 1. Sociodemographic characteristics in 2020–2021 and 2021–2022, age, seniority in teaching, BMI and absenteeism, and correlation with quality of working life (QWL).
QWL
Mean (SD)Rp
2020–2021Age (years) (n = 220)40.48 (8.21)−0.1270.060
Seniority in teaching (years) (n = 210)12.50 (7.80)−0.0540.437
BMI (kg/m2) (n = 220)26.56 (3.61)−0.0310.651
Absenteeism (days) (n = 220)2.19 (13.67)−0.0840.213
2021–2022Age (years) (n = 166)41.39 (8.83)−0.1990.010 *
Seniority in teaching (years) (n = 162)11.08 (6.29)−0.2100.007 *
BMI (kg/m2) (n = 164)27.66 (4.15)−0.0050.946
Absenteeism (days) (n = 169)3.02 (8.97)−0.289<0.001 *
All variables were characterized by the mean (SD). * p < 0.050.
Table 2. Sociodemographic and lifestyle characteristics in 2020–2021 and 2021–2022, biological sex, contract type, teleworking and smoking habits, and quality of working life (QWL) differences between categories.
Table 2. Sociodemographic and lifestyle characteristics in 2020–2021 and 2021–2022, biological sex, contract type, teleworking and smoking habits, and quality of working life (QWL) differences between categories.
QWLTest Statisticp
Mean (SD)
2020–2021GenderFemale (n = 103)3.81 (0.62)−2.131 a0.034 *
Male (n = 117)3.99 (0.61)
Contract TypeStable (n = 182)3.87 (0.62)−1.856 a0.065
Temporary (n = 37)4.07 (0.58)
TeleworkingNo (n = 38)3.95 (0.67)1.417 b0.492
Yes, hybrid (n = 113)3.90 (0.65)
Yes, permanent (n = 69)3.89 (0.55)
Smoking HabitsSmoker (n = 6)3.86 (0.73)0.362 b0.834
Non-smoker (n = 192)3.90 (0.61)
Ex-smoker (n = 22)3.96 (0.68)
2021–2022GenderFemale (n = 93)3.12 (0.41)−1.958 a0.053
Male (n = 73)3.29 (0.60)
Contract TypeStable (n = 131)3.19 (0.52)−0.090 a0.924
Temporary (n = 35)3.20 (0.47)
TeleworkingNo (n = 9)3.50 (0.59)6.856 b0.032 *
Yes, hybrid (n = 97)3.25 (0.50)
Yes, permanent (n = 60)3.06 (0.47)
Smoking HabitsSmoker (n = 38)3.12 (0.51)11.725 b0.003 *
Non-smoker (n = 107)3.28 (0.50)
Ex-smoker (n = 21)2.89 (0.41)
All variables were characterized by the mean (SD). a Independent-sample t-test. b Kruskal–Wallis test. * p < 0.050.
Table 3. Environmental characteristics in 2020–2021 and 2021–2022, difficulties with materials or physical resources, difficulties with technological support, noise temperature in summer and light, and quality of working life (QWL) differences between categories.
Table 3. Environmental characteristics in 2020–2021 and 2021–2022, difficulties with materials or physical resources, difficulties with technological support, noise temperature in summer and light, and quality of working life (QWL) differences between categories.
QWLTest Statisticp
Mean (SD)
2020–2021Difficulties with materials or physical resources Never (n = 134)4.02 (0.55)11.242 a<0.001 *
Seldom (n = 46)3.76 (0.65)
Always (n = 26)3.46 (0.64)
Difficulties with technological supportNever (n = 152)3.98 (0.58)6.349 a0.002 *
Seldom (n = 36)3.66 (0.61)
Always (n = 18)3.60 (0.72)
NoiseComfortable (n = 153)3.99 (0.56)5.700 a0.004 *
Moderate (n = 36)3.69 (0.66)
Uncomfortable (n = 27)3.66 (0.75)
LightingComfortable (n = 192)3.92 (0.60)3.394 b0.183
Moderate (n = 19)3.65 (0.70)
Uncomfortable (n = 5)3.94 (0.80)
Temperature in summerComfortable (n = 154)3.99 (0.54)6.793 a0.001 *
Moderate (n = 43)3.65 (0.76)
Uncomfortable (n = 19)3.66 (0.67)
2021–2022Difficulties with materials or physical resourcesNever (n = 143)3.23 (0.48)4.295 b0.117
Seldom (n = 11)3.06 (0.47)
Always (n = 5)2.81 (1.16)
Difficulties with technological supportNever (n = 142)3.25 (0.50)8.308 b0.016 *
Seldom (n = 8)2.85 (0.44)
Always (n = 6)2.63 (0.51)
NoiseComfortable (n = 84)3.33 (0.59)5.936 a0.003 *
Moderate (n = 33)3.12 (0.46)
Uncomfortable (n = 47)3.03 (0.51)
LightingComfortable (n = 79)3.37 (0.57)9.564 a<0.001 *
Moderate (n = 48)3.09 (0.37)
Uncomfortable (n = 36)2.99 (0.38)
Temperature in summerComfortable (n = 57)3.36 (0.63)7.555 b0.023 *
Moderate (n = 21)3.30 (0.52)
Uncomfortable (n = 86)3.07 (0.36)
All variables were characterized by the mean (SD). a ANOVA. b Kruskal–Wallis test. * p < 0.050.
Table 4. Psychosocial factors and musculoskeletal symptoms in 2020–2021 and 2021–2022, satisfaction with supervisors, students and colleagues, neck and lower back symptoms in the last 12 months, and quality of working life (QWL) differences between categories.
Table 4. Psychosocial factors and musculoskeletal symptoms in 2020–2021 and 2021–2022, satisfaction with supervisors, students and colleagues, neck and lower back symptoms in the last 12 months, and quality of working life (QWL) differences between categories.
QWLTest Statisticp
Mean (SD)
2020–2021Satisfaction with supervisors Satisfied (n = 164)4.07 (0.50)28.886 a<0.001 *
Neutral (n = 38)3.39 (0.48)
Dissatisfied (n = 18)3.48 (1.00)
Satisfaction with studentsSatisfied (n = 167)3.51 (0.76)14.575 a<0.001 *
Neutral (n = 33)3.53 (0.63)
Dissatisfied (n = 20)3.51 (0.76)
Satisfaction with colleaguesSatisfied (n = 172)4.03 (0.55)20.339 a<0.001 *
Neutral (n = 27)3.32 (0.45)
Dissatisfied (n = 21)3.64 (0.84)
Neck symptoms in the last 12 monthsNo (n = 137)4.02 (0.63)3.640 b<0.001 *
Yes (n = 83)3.71 (0.56)
Lower back symptoms in the last 12 monthsNo (n = 115)4.04 (0.61)3.468 b<0.001 *
Yes (n = 105)3.78 (0.60)
2021–2022Satisfaction with supervisors Satisfied (n = 61)3.43 (0.54)11.781 a<0.001 *
Neutral (n = 79)3.10 (0.38)
Dissatisfied (n = 26)2.96 (0.57)
Satisfaction with studentsSatisfied (n = 64)3.44 (0.56)13.263 a<0.001 *
Neutral (n = 75)3.04 (0.39)
Dissatisfied (n = 27)3.05 (0.48)
Satisfaction with colleaguesSatisfied (n = 52)3.50 (0.58)16.008 a<0.001 *
Neutral (n = 79)3.06 (0.37)
Dissatisfied (n = 35)3.04 (0.47)
Neck symptoms in the last 12 monthsNo (n = 55)3.32 (0.56)2.286 b0.012 *
Yes (n = 111)3.13 (0.47)
Lower back symptoms in the last 12 monthsNo (n = 54)3.27(0.50)1.277 b0.204
Yes (n = 112)3.16 (0.51)
All variables were characterized by the mean (SD). a ANOVA. b Independent-sample t-test. * p < 0.050.
Table 5. Correlations between work–family conflict, stress, depression, anxiety and sleep disturbances in 2020–2021 and in 2021–2022.
Table 5. Correlations between work–family conflict, stress, depression, anxiety and sleep disturbances in 2020–2021 and in 2021–2022.
Work–Family
Conflict		Stress
Perception		Anxiety
Perception		Depression
Perception		Sleep
Disturbances
2020–2021Work–family conflict (n = 220)1
Stress perception (n = 220)0.339 **1
Anxiety perception (n = 220)0.275 **0.809 **1
Depression perception (n = 220)0.254 **0.746 **0.650 **1
Sleep disturbances (n = 217)0.230 **0.572 **0.559 **0.537 **1
2021–2022Work–family conflict (n = 166)1
Stress perception (n = 153)−0.1051
Anxiety perception (n = 153)−0.1850.731 **1
Depression perception (n = 153)−0.1770.667 **0.793 **1
Sleep disturbances (n = 166)0.244 **−0.021−0.007−0.0311
** p < 0.001.
Table 6. Psychosocial and health-related factors in 2020–2021 and 2021–2022, work–family conflict, stress, depression, anxiety and sleep disturbances, and correlation with quality of working life (QWL).
Table 6. Psychosocial and health-related factors in 2020–2021 and 2021–2022, work–family conflict, stress, depression, anxiety and sleep disturbances, and correlation with quality of working life (QWL).
QWL
Mean (SD)Rp
2020–2021Work–family conflict (n = 220)2.76 (0.97)−0.300<0.001 *
Stress perception (n = 220)19.46 (7.98)−0.462<0.001 *
Anxiety perception (n = 220)18.28 (6.53)−0.380<0.001 *
Depression perception (n = 220)18.72 (7.25)−0.549<0.001 *
Sleep disturbances (n = 217)4.74 (4.04)−0.550<0.001 *
2021–2022Work–family conflict (n = 166)2.49 (0.82)−0.437<0.001 *
Stress perception (n = 153)17.29 (6.25)0.0770.345
Anxiety perception (n = 153)18.85 (5.88)0.0470.567
Depression perception (n = 153)19.25 (6.54)0.0420.608
Sleep disturbances (n = 166)6.06 (3.59)−0451<0.001 *
All variables were characterized by the mean (SD). * p < 0.050.
Table 7. Multiple linear regression model for the quality of working life in the data collection period of 2020–2021.
Table 7. Multiple linear regression model for the quality of working life in the data collection period of 2020–2021.
PredictorB (SE)t or Fp95% CI for BR2 Cont.VIF
Constant4.516 (0.157)28.838<0.001(4.207, 4.824)––
Diff. with materials or physical resources a–6.7930.001–0.048–
Diff. with materials or physical resources (Never)0.346 (0.104)3.3210.001(0.140, 0.551)–2.510
Diff. with materials or physical resources (Seldom)0.408 (0.116)3.516<0.001(0.179, 0.636)–2.420
Satisfaction with supervisors b–15.400<0.001–0.125–
Satisfaction with supervisors (Unsatisfied)−0.147 (0.121)−1.2150.226(−0.385, 0.091)–1.148
Satisfaction with supervisors (Neutral)−0.470 (0.085)−5.547<0.001(−0.637, −0.303)–1.122
Pittsburgh score−0.045 (0.010)−4.729<0.001(−0.064, −0.026)0.1581.544
Depression score−0.018 (0.005)−3.345<0.001(−0.029, −0.008)0.1151.610
Work–family conflict−0.104 (0.033)−3.1060.002(−0.169, −0.038)0.0551.111
a The reference level was “Always”. b The reference level was “Satisfied”.
Table 8. Multiple linear regression model for the quality of working life in the data collection period of 2021–2022.
Table 8. Multiple linear regression model for the quality of working life in the data collection period of 2021–2022.
PredictorB (SE)t or Fp95% CI for BR2 Cont.VIF
Constant3.128 (0.259)12.074<0.001(2.616, 3.640)––
Gender (Female) a0.148 (0.067)2.1980.030(0.015, 0.281)0.0241.268
Diff. interpersonal relationship supervisors b–5.7530.004–0.070–
Diff. interpersonal relationship supervisors (Never)0.509 (0.163)3.1200.002(0.187, 0.831)–1.790
Diff. interpersonal relationship supervisors (Seldom)0.231 (0.250)0.9260.356(−0.262,0.724)–1.750
Smoke c–3.1560.046–0.039–
Smoke (Non-smoker)0.171 (0.082)2.0760.040(0.008, 0.333)–1.755
Smoke (Ex-smoker)−0.020 (0.108)−0.1850.854(−0.232, 0.193)–1.517
Satisfaction with students d–7.0740.001–0.085–
Satisfaction with students (Unsatisfied)−0.243 (0.093)−2.6210.010(−0.426, −0.060)–1.301
Satisfaction with students (Neutral)−0.246 (0.069)−3.572<0.001(−0.381, −0.110)–1.322
Noise e–4.0120.020–0.041–
Noise (Comfortable)0.201 (0.071)2.8180.006(0.060, 0.342)–1.434
Noise (Moderate)0.145 (0.088)1.6530.101(−0.028, 0.319)–1.422
Pittsburgh score−0.036 (0.009)−3.995<0.001(−0.054, −0.018)0.1221.235
Work–family conflict−0.196 (0.039)−4.977<0.001(−0.273, −0.118)0.1391.177
a The reference level was “Male”. b The reference level was “Always”. c The reference level was “Smoker”. d The reference level was “Satisfied”. e The reference level was “Uncomfortable”.
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Paula, V.M.d.; Teles, J.; Cotrim, T.P. Organizational and Individual Factors Influencing the Quality of Working Life Among Brazilian University Professors during COVID-19. Sustainability 2024, 16, 6351. https://doi.org/10.3390/su16156351

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Paula VMd, Teles J, Cotrim TP. Organizational and Individual Factors Influencing the Quality of Working Life Among Brazilian University Professors during COVID-19. Sustainability. 2024; 16(15):6351. https://doi.org/10.3390/su16156351

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Paula, Vanessa Molinero de, Júlia Teles, and Teresa Patrone Cotrim. 2024. "Organizational and Individual Factors Influencing the Quality of Working Life Among Brazilian University Professors during COVID-19" Sustainability 16, no. 15: 6351. https://doi.org/10.3390/su16156351

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