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Propósitos y Representaciones
versión impresa ISSN 2307-7999versión On-line ISSN 2310-4635
Propós. represent. vol.11 no.3 Lima set./dic. 2023 Epub 31-Dic-2023
http://dx.doi.org/10.20511/pyr2023.v11n3.1812
RESEARCH ARTICLES
Teaching Performance: The Consequences of Burnout and Its Relation to Protective Factors
1Universidad de Lima, Facultad de Psicología, Lima, Perú
The burnout syndrome is an occupational health indicator that intervenes in the teaching-learning process and may harm the quality of education offered by institutions. The relationship between individual characteristics and the work environment may have different effects over the teaching performance. The current investigation seeks to analyze the fit of a model to predict teaching performance based on the teacher burnout, while including individual characteristics and the work environment perception in university teachers. The sample is composed of 94 teachers who provided information related to the Conscientiousness trait, their burnout and engagement levels, their teaching performance, and their perception of the work environment. This quantitative and cross-sectional research makes use of structural equation models to test its hypothesis. The second tested model showed the best fit, considering teaching performance as different variables for each component. This model shows that the perception of the work environment is the main predictor of performance (β = .116 - .239). The implications of these findings are discussed, focusing specially on performance as a construct.
Keywords: Performance; Teacher burnout; Work environment; Human resources; Structural equation models
Introduction
Performance is a variable of great relevance in the field of organizational psychology given that it encompasses all the actions, behaviors and results that a person generates based on the goals set by the organization (Viswesvaran & Ones, 2017). To better define it, it can be considered as the degree of execution that an individual achieves in pursuing the objectives of his or her position in a given period through the use of his or her skills (Quero et al., 2014). Specifically, teaching performance is related to the observable pedagogical practice as a reflection of the teacher's competence to face, in a specific way, the demands required by the teaching-learning process (Gálvez & Milla, 2018; Tapia & Tipula, 2017). Despite their theoretical definitions, there is still no agreement on their structure, the relevance of the unit of analysis used, or the appropriateness of different measurement strategies (Hornstein, 2017; Viswesvaran & Ones, 2017; Whiteley, 2016). In fact, the approach used to evaluate it can distort its definition. For example, through tools focused on results obtained, focused on the behavior of the teacher in the classroom, or focused on the development of the teacher and the revisions he/she makes in his/her method (Gálvez & Milla, 2018). This complication even derives in the use of tools that do not lead to valid inferences about teaching performance (Hornstein, 2017; Whiteley, 2016). Understanding this construct becomes more complicated when considering certain conditions of the teaching job related to pressure, the diversity of "clients", the possible existence of value conflicts, among others (Poulou, 2017). The demands that this work brings include demonstrating emotional stability and regulation, classroom climate management, mastery of the subject matter, pedagogical skills and skills linked to the evaluation process, as well as the use of ICTs (Barreiro & Bozutti, 2017; Hosseinnia et al., 2019; Jeung et al., 2018; Lee et al., 2016; Singh et al., 2020; Villaroel & Bruna, 2017).
To adequately predict this, different variables must be taken into account. The work context, which encompasses characteristics of the work environment, influences in different ways. For example, social support or autonomy enhance performance, while overload or ambiguity of functions and roles, may impair it (Trépanier et al., 2015; Wen et al., 2020). The organization's climate and culture, both of which have an effect on organizational productivity, should also be taken into account (Aguirre, 2011). These constructs are complex at the epistemological level since their definition and delimitation have presented obstacles linked to their objective or subjective nature, the relevant level of analysis, and the assessment methods required (Ostroff et al., 2013). Despite this, there is consensus to define organizational culture as an entity composed of accepted behaviors, basic assumptions, and shared learning and meanings, manifested and reinforced through formal and informal mechanisms (Rivera et al., 2018). Organizational climate is defined through the perceptions that workers have about the procedures, practices and policies that are maintained in the institution (Veloso-Besio et al., 2015).
At the level of individual differences, personality is addressed through the Big Five Factor model, which has demonstrated stability and predictive capacity on behavior and, in organizations, on job performance (McCrae & Costa, 1985; Pop, 2013). In the work environment, extroversion is positively related to building social relationships, desirable work climate, and motivation in other people (Lorenzo, 2016). However, Neuroticism is negatively related to job performance, motivation and goal setting (Judge & Ilies, 2002). Kindness has been shown to be related to high levels of work engagement and organizational citizenship behaviors (Smith & DeNunzio, 2020). Openness is also positively related to the latter. Finally, Conscientiousness has been shown to be related to greater receiving of training, higher job performance and career success (Barrick & Mount, 1991; Woods et al., 2013). However, this trait is also a direct predictor of overcommitment, a variable linked to work addiction and negative effects on mental health (Huyghebaert et al., 2017).
The work context and its relationship with individual characteristics can have an impact on teaching performance and occupational health and can lead to negative consequences such as burnout syndrome or positive consequences such as increased engagement or organizational commitment (Buil et al., 2018; Caniëls et al., 2018; Dhir & Shukla, 2019; Pendersen & Minnote, 2016).
Occupational health of teachers should also be a focus of attention, since it can affect the educational agent, the students, and the quality of the system (Espinoza-Díaz et al., 2015; Reyes, 2016; Wilson, 2002). The high demands, emotional demands, role conflict and other characteristics of teaching place it among the most stressful jobs (Stelmokiene et al., 2019). The "human services", in fact, are characterized by reduced expectation of control and rewards from work (Carlín & Garcés, 2010; Meier, 1983). This type of work can affect the self-esteem and self-efficacy of the employee (Leupold et al., 2020); as well as require emotional effort from the professional (Lee, 2017). These job characteristics, specific to the education and health sector, may increase the risk of burnout syndrome (Jeung et al., 2018). This syndrome can be understood as an inadequate response to chronic stress with three main components: emotional exhaustion in the face of excessive workload; depersonalization, as a coping strategy; and lack of job fulfillment, as a final consequence (Maslach et al., 2001; Rivera et al., 2018; Sofologi et al., 2018).
This syndrome manifests itself in different ways, including irritability, hostility, loss of creativity, disorientation, avoidance of responsibilities, social isolation, sleep problems, sexual dysfunction, increased blood pressure, and elevated consumption of caffeine, alcohol, or tobacco (Carlín 2014; Iancu et al., 2018; Raja et al., 2018). Dhir and Shukla (2019) consider it important to examine the institutional context; with variables that are related to burnout such as organizational leadership (Buil et al., 2018; Caniëls et al., 2018) or psychosocial climate (Pendersen & Minnote, 2016). Other authors consider it relevant to pay attention to individual differences (Zysberg et al., 2016) through personal resources such as organizational commitment (Gagné et al., 2008), psychological capital (Demir, 2018; Malinen & Savolainen, 2016) or personality (Castillo-Gualda et al., 2019).
Engagement or organizational commitment maintains similar characteristics to burnout syndrome, although in the opposite way (Spontón et al., 2018). Engagement is at the opposite end of the same emotional continuum but differs from burnout in that it is highly changeable, varying even throughout the day (Bakker et al., 2011). This concept has received explanations in models similar to those used to understand burnout, including the Needs and Satisfaction Model, Social Exchange Theory, and the Demands and Resources Model (Rattrie et al., 2020; Sun & Bunchapattanasakda, 2019). Organizational commitment has three components: vigor, linked to effort; dedication, reflected in pride; and absorption at work, linked to happiness at work (Salanova et al., 2018). It manifests in high performance and high productivity (Alessandri et al., 2018). However, overcommitment can result in family conflict, unethical behaviors, and territorial work behavior (Wang et al., 2019).
The literature provides interesting information regarding the interaction of these variables. For example, chronic work stress does not necessarily imply low levels of engagement, but both could coexist (De Chávez et al., 2014). There is evidence of teaching profiles characterized by high levels of engagement and burnout, possibly originated by high workload, economic necessity, and low autonomy over work (Salmela-Aro et al., 2019). This profile can lead to overcommitment and work addiction, which in turn can generate emotional exhaustion and diminished performance (Huyghebaert et al., 2017; Upadyaya et al., 2016). These variables are usually understood within the Job Demands and Resources model, which proposes that they arise as a result of the interaction between job demands and resources of the person and the work context (Yin et al., 2018).
Regarding personality, neuroticism is positively related to burnout; while the rest of the factors show protective effects (Ang et al., 2016). Similar but opposite results have been found when examining their relationship with engagement (Sadeghi et al., 2015). In general, these results are common when performing variable-centered analysis (Khoeini & Attar, 2015; Ziapour & Kianipour, 2015) and in person-centered analyses, such as latent profile analyses (Conte et al., 2017; Perera et al., 2018).
The literature has also shown that some variables usually considered "dependent" may have a predictive role. Engagement, for example, has been shown to mediate the relationship of perceived organizational prestige and job performance (Dhir & Shukla, 2019). Burnout has begun to be studied as a predictor of variables such as performance, discourtesy in the work environment, and even turnover (Pan, 2017; Rahim & Cosby, 2016).
As has been shown, the prediction of performance is a complex task and requires the analysis of its nomological network for its understanding since psychological variables present difficulties to be analyzed in isolation. In today's world, the study of variables that may favor or detriment performance is of high relevance (Sanin & Salanova, 2014). The present research aims to review the fit of a multivariate model to predict performance. We wish to verify whether there is a statistically significant correlation between the perception of the work context and burnout syndrome (H1), engagement (H2), and teaching performance (H3). In addition, we wish to verify if there is a mediation of Conscientiousness between the relationship between burnout and the perception of the context (H4); as well as between the relationship between engagement and teaching performance (H5). Finally, we wish to verify whether there is a mediation of commitment between the relationship between burnout and teaching performance (H6).
Method
Design
This empirical research has an associative strategy and can be considered an explanatory study since its objective is to understand how different variables are related to teaching performance through a structural equation model based on the Resources and Labor Demands model. In addition, this explanatory study is developed in a cross-sectional manner through an explanatory design with latent variables with measurements taken at the same time (Ato et al., 2013).
Participants
The population is composed of university higher education teachers. According to the stipulations of the University Personnel Department of the higher education institution that was contacted, the sampling frame was defined, and a non-probability and convenience process was carried out with the objective of collecting data from a complete faculty according to the previously defined sampling frame (Hernández & Mendoza, 2018). Inclusion criteria were defined as having a work relationship with teaching hours during the evaluation period; and, they had to sign an informed consent form related to the research objectives and data handling if they wished to be volunteers. Based on this, a sample of 94 teachers was reached, of which 35 were men and 59 were women. Of the sample, 41.49% were single; 47.87% were married; and the remaining 10.63% were divorced or widowed. In addition, the average time of experience in higher education of the teachers was 10.85 years (DE = 8.14).
Instruments
This research study uses indicators of burnout syndrome, engagement, awareness factor, perception of the work context and performance. For this purpose, psychometric questionnaires and questionnaires provided by the institution to which the evaluated persons belong are used.
First, the Revised Teacher Burnout Questionnaire [CBP-R] makes use of items related to stress and burnout syndrome in the education sector. The scores obtained are grouped into three factors. The first factor is composed of two subscales linked to the conditions that the teacher maintains in their workplace. The second factor is composed of items specifically related to stress and burnout syndrome. The questionnaire is composed of 66 Likert-type items with five anchors. However, for the purposes of this research, only the 19 items specific to burnout syndrome are used. After reviewing the quality of the scores obtained with this one, it is found that the three-factor solution is adequate, with loadings greater than .30, acceptable fit according to one of the indices (RMSR = .061, RMSEA = .455), and high reliability (ω = .957).
The engagement scale used was the Ultrech Work Engagement Scale [UWES] composed of 17 Likert-type items with seven frequency anchors. Flores et al. (2015) have found evidence of validity of the scores obtained in a similar sample, with results consistent with those reported in Europe (Salanova et al., 2018; Schaufeli et al., 2006) and Latin America (Juárez-García et al., 2015). In the present sample, the structure of the scores can be reduced to the three factors proposed in the original design, with high reliability (ω = .945) and acceptable fit according to one of the indices (RSMR = .067; RMSEA = .586).
The Conscientiousness factor was evaluated based on the Big Five Questionnaire [BFQ]. Only items reflecting the Conscientiousness factor were used. These items refer to behaviors of persistence, responsibility, and care in different situations. In a similar population, Dominguez-Lara et al. (2014) have shown evidence of validity linked to construct structure. In the present sample, after analyzing the factorization of the scores obtained with this subscale, it was found that it was composed of two factors, the facets proposed in its original design and with an acceptable fit (RMSR = .092, RMSEA = .074). In addition, the reliability of the scores remains within acceptable for research purposes (ω = .795).
The perception of the work context was evaluated through the instrument used by the University Personnel Department. These data, only required after obtaining the consent of the participants, were obtained with a questionnaire of 47 Likert-type items with five anchors linked to the level of satisfaction. These scores are presented in four dimensions: Academic Training, Teacher Management, Administrative Services, and Global Opinion. In the present sample, the factorial solution was found to have acceptable fit according to one of the indices (RMSR = .064, RMSEA = .493) and high reliability (ω = .982).
Teaching performance was also evaluated with the tool used by the institution. This is composed of 17 items linked to four factors: Subject Mastery, Didactic Skills, Classroom Climate and Pedagogical Standards. These scores are the average of heteroevaluations made by the students. It has items related to the competencies that the teacher uses, the management of the social environment in the classroom, the management of discipline and the management of the agreements generated in class. In the present sample, the scores showed an excellent fit to the four-factor model according to one of the indices (RMSR = .019, RMSEA = .178) and high reliability (ω = .99).
Procedure
Following the approval of the Institutional Ethics Committee and the Personnel Department of the educational institution to conduct research on teachers and collect data related to the perception of the context and job performance, the teachers were contacted and received an informed consent form detailing the objectives of the research and requesting access to performance data and work context. Only after the acceptance of each participant did we proceed to the application of the questionnaires. These were applied digitally as a security measure against the current pandemic. The data related to performance evaluation and work context were provided by the University Personnel Department. The data were systematized in an electronic database without data that could lead to the identification of the participants.
Data analysis
The analyses were performed using R programming software and the packages psych (Revelle & Revelle, 2015) and lavaan (Rosseel, 2012). The fit of the data to the factor analysis and its factor structure based on the polychoric matrix were reviewed (Burga, 2006; Ferrando & Anguiano-Carrasco, 2010; Parry, 2017; Watson, 2017). In addition, its reliability was reviewed using the omega index (McNeish, 2017; Peters, 2014). After the review of assumptions linked to the distribution, performed with the MVN package (Korkmaz et al., 2014), we proceeded to the structural equation analysis in two stages: confirmatory factor analysis, which considers the measurement model with all possible correlations and represents the best possible fit of the model; and structural relationship analysis, where we proceed to analyze the hypotheses proposed and the fit of the specific model (Brown, 2006; Hair et al., 2014). As reviewed in the theory (Keith, 2014), the following indices will be used to review the fit of the models: Comparative Fit Index (CFI ≥ .90, acceptable; CFI ≥ .95, excellent); Tucker Lewis Index (TLI ≥ .90, acceptable; TLI ≥ .95, excellent); Root Mean Squared Error of Approximation (RMSEA ≤ .08, acceptable; RMSEA ≤ .05, excellent) y Standardized Root Mean Square Residual (SRMR ≤ .08, acceptable; SRMR ≤ .05, excellent).
Results
The first measurement model contemplates all the proposed variables and shows an acceptable fit; RSMR = .073, RMSEA = .070, TLI = .931, AGFI = .781. Except for the Persistence indicator in the Conscientiousness trait, the other indicators have statistically significant loadings. The correlations found between the variables show coefficients of varying size, reaching even moderate sizes for a correlation (.011 - .417). This measurement model also shows a low number of standardized residuals with an absolute value greater than 2.5, indicating that the model adequately explains the variance in the data.
With respect to the structural model proposed, it showed an acceptable fit to the data in most of the indexes; RSMR = .073, RMSEA = .069, TLI = .93, AGFI = .78. A positive, small and statistically insignificant relationship was found (β = .171, p = .141) between the perception of context and burnout syndrome. (H1). Regarding the relationship between context and engagement (H2), an inverse correlation was found, which was small and not statistically significant (β = -.232, p = .110). Regarding the relationship between context and teaching performance (H3), there is insufficient evidence to support its existence. (β = .023, p = .852). Regarding the mediation of awareness in the relationship between burnout and context (H4), the evidence is insufficient to indicate that it exists. The relationship between awareness and perception of context showed inadequate practical and statistical significance (β = .045, p = .762); the relationship between burnout and conscientiousness was negative and small (β = -.169, p = .286); and the indirect effect of burnout on the perception of the context was practically null (β = -.008, p = .772). The results relevant to the mediation of awareness in the relationship between commitment and teaching performance (H5) were similar. Direct relationship between awareness and performance showed null results (β < .001, p = .996); the relationship between commitment and awareness was moderate, but lacked statistical significance (β = .369, p = .253); while the indirect effect of commitment on performance yielded null results (β < .001, p = .996). The same is true for the mediation of engagement in the relationship between burnout and performance (H6). The relationship between burnout and engagement was negative, moderate and not statistically significant (β = -.338, p = .388). The relationship between commitment and teaching performance was null (β = .031, p = .862), and the indirect effect of burnout on performance as well (β = -.010, p = .864). While the fit of the model appears to be adequate, this may be due to the large number of relationships contemplated, which is evidenced by a slight increase in the large residuals (See Figure 1 and Figure 2).
A re-specification of the model is generated, temporarily discarding the performance variable, and considering engagement and burnout as dependent variables according to the literature and the results of the first model. Although this analysis does not contemplate the main objective of analyzing the relationships between these variables and teaching performance, it allows for a more simplified model to analyze the correlations that exist between the rest of the variables. This model itself is an exploration of the theory prior to the final re-specification of the model that includes teaching performance. This second measurement model shows an acceptable fit to the data; RSMR = .075, RMSEA = .041, TLI = .969, AGFI = .852. This could be due to the greater parsimony of this model. The indicators show moderate to large significant loadings (.455 - .973) on their respective latent variables. In addition, the results linked to the residuals are found to be adequate. The second structural model yields an acceptable and excellent fit to the data; RSMR = .075, RMSEA = .041, TLI = .969, AGFI = .852 (See Figure 3 and Figure 4).
Specifically, a small positive correlation can be found between burnout and perception of context (H1), although it is not statistically significant. The same is true for the relationship between context and engagement, although it is negative (H2). Although mediations are not reviewed, other results pertain to the following hypotheses. For example, a negative, moderate and statistically significant relationship is found between conscientiousness and burnout (H4); and, moderate and positive with engagement (H5), although it is not statistically significant (β = .503, p = .086). The relationship between burnout and engagement (H6) was relatively small and was not statistically significant (r = -.221, p = .429).
Finally, a third re-specification is generated to consider the relationship with teaching performance, although it is now separated into four dimensions. It was decided to maintain burnout, engagement, and the perception of the context based on the global opinion of the context as predictors. This measurement model showed acceptable fit; RSMR = .056, RMSEA = .083, TLI = .933, AGFI = .717. In addition, it showed moderate to large loadings (.424 - .996) on its latent variables.
This third structural model takes up the teaching performance, contemplated in its dimensions: Subject mastery, Pedagogical standards, Classroom climate and Methodology. Its fit to the data is acceptable; RSMR = .056, RMSEA = .083, TLI = .933, AGFI = .717. Some results with low statistical and practical significance (H1 and H2) are found, such as the effect of context perception on burnout (β = .049, p = .611); and the relationship between context and engagement (β = -.020, p = .885). The relationship between context and performance (H3) must now be observed in terms of each dimension. In general, the relationships found are small and positive with subject mastery (β = .239, p = .125), management of pedagogical standards (β = .116, p = .374), classroom climate (β = .137, p = .328), and methodology (β = .236, p = .103). Moreover, the relationships of interest for the remaining hypotheses (H5 and H6) yielded interesting results. Engagement was shown to be positively correlated, albeit small, with subject mastery; (β = .152, p = .343); but, null with the other dimensions of teaching performance. (Methodology = -.031, Classroom climate = -.006, Pedagogical standards= .011). Finally, the relationship between burnout and pedagogical standards was positive and small (β = .105, p = .386); although its relationship with the other dimensions was practically null (Methodology = -.014, Classroom climate = .017, Subject mastery = .073) (See Figure 5 and Figure 6).
Discussion
The results found in this study allow us to conclude that the main predictor of teaching performance is the perception of the work environment, which most consistently affects all the components of performance. Understanding these theoretical models, however, is not so simple and requires analyzing the results in terms of the hypotheses put forward and the interaction of the variables within the model. The first hypothesis supported the existence of an effect of context on burnout. Although the first model showed a positive relationship between the two variables, this contradicts what has been proposed by other authors (Milan et al., 2020; Pecino et al., 2019; Seyyedmoharrami et al., 2019;). However, it is possible that the content of the instrument relates more to organizational demands and thus have a positive effect on the syndrome (Borst et al., 2019).
Some of the indicators included in this instrument refer to satisfaction with institutional policies and procedures, which have been found to be negatively related to engagement and could, therefore, be positively related to burnout (Li et al., 2015). In the third model, it is decided to make use only of the indicators referring to the overall opinion of the context since the grouping of some variables may impair their understanding (Skaalvik & Skaalvik, 2018). In this model the relationship is null and not statistically significant, so there is not enough evidence to make statements regarding it. Similar results are found for the second hypothesis. This shows the existence of a relationship between context and engagement. Although the results have little practical and statistical significance, it is important to note that there is a stronger association between these two variables than with burnout. This result coincides with that found by Milan and his collaborators (2020). The third hypothesis proposes a direct effect of the perception of context on teaching performance. However, the results found in the first and third models indicate null or small effects, even when making use of global opinion indicators. However, it is possible that this relationship is mediated by variables such as engagement (Dhir & Shukla, 2019) or affected by irrelevant variance resulting from performance evaluation (Kim et al., 2017). It is emphasized that the magnitude of the relationships between context and performance increased when working with the dimensions in a decoupled manner.
The following hypotheses propose the existence of different mediations. The role of the Conscientiousness personality factor as a mediator is not clear from the results obtained. Interesting results were found, such as its moderate and inverse relationship with burnout, which could result from the association between increased job resources and this personality factor (Ang et al., 2016). In the second model, after discarding the mediating role, conscientiousness was shown to be positively and moderately correlated with engagement. This may be due to the patterns of efficiency, responsibility and perseverance inherent to this factor and is consistent with previous research (Conte et al., 2017; Perera et al., 2018; Sadeghi et al., 2015). It is possible that this factor plays an antecedent role in organizational resources that in turn affect burnout and engagement levels. The last hypothesis placed engagement as a protective factor of teaching performance against the effect of burnout. The results of the first model do not provide sufficient evidence to support this hypothesis. It is possible that engagement is a mediator between organizational resources and performance, but not burnout (Dhir & Shukla, 2019). In the third model, the direct effect of engagement on performance is found to depend on the specific dimension. For example, the relationship with subject mastery may be due to the desire to manage the content to be taught, which has been found by other authors (van Wingerden & Van der Stoep, 2018). Regarding the effect of burnout on performance, a positive relationship can be found with the management of pedagogical standards. This dimension is related to compliance with agreements linked to the evaluation method, evaluation dates, delivery of results and handling claims, which may involve moments of exposure to stress. As Braun and collaborators (2017) have pointed out, not all competencies are impaired due to the presence of burnout.
It is important to take into account the results of this research, especially in the case of the re-specifications, as exploratory and not definitive analyses in relation to these variables. Other research also considers that analyses should not only be carried out at the variable level, but also at the individual level, considering the effect of different profiles on variables such as teaching performance, engagement and burnout (Conte et al., 2017; Huyghebaert et al., 2017; Perera et al., 2018; Salmela-Aro et al., 2019). In addition to the search for data collection in larger sample sizes, the suggestion of performing latent variable analysis may be an important route for progress in the field of education and labor welfare, especially when working on variables that may affect educational quality.
Acknowledgments
We thank the research participants for their time.
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Received: April 08, 2023; Revised: April 13, 2023; Accepted: October 13, 2023; pub: December 31, 2023
Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
