A longitudinal examination of the relationship between teacher burnout and depression.
Teaching is one of the hardest jobs leading to excessive stress levels (Cacha, 1981; Pines, 2002). Students with behavioral problems, conflict with coworkers, parent-teacher relationship problems, or adapting new teaching methods are representative examples of stressors inherent in a teaching job (Skaalvik & Skaalvik, 2007). Being a teacher is getting even tougher for Korean teachers because they are currently in the midst of a cultural transition. Ever since the establishment of the Republic of Korea in 1948, education reforms have been constantly undertaken and the average numbers of students in classrooms have gradually been reduced (Ministry of Education, 2000). Despite the improved physical classroom environment, Korean people cast doubt on the teaching and learning process in the school system. The majority of parents who are not satisfied with the quality of education their children receive from school instead send their children to several after-school private programs (M. Kim, 2003). Korean students' heavy dependence on out-of-school educational services, in turn, inevitably keeps students from being motivated in the school classroom. From the teachers' perspective, on the other hand, teaching students is getting harder because they show less interest in school activities, less respect for school disciplines, and even less willingness to attend school (M. Kim, 2003). Because older generations from East Asian cultural settings (e.g., China, Japan, Korea) share a Confucian heritage with a strong emphasis on harmony and respect for elders (U. Kim & Park, 2006), Korean teachers naturally expect respect from students, to some extent. Disparities between teachers' expectations and students' actual behaviors are, thus, also a great source of Korean teachers' stress. All of these conditions contribute to Korean teachers' experience of frustration, helplessness, and sometimes anger toward students' behaviors, leading to a general lack of motivation.
Most teachers try to cope with such stressors by means of active problem solving, seeking social and emotional support from coworkers, cooperating with parents, reorganizing the teaching environment, or changing their teaching strategy. However, continued failure of coping with such chronic stressors may precipitate burnout among teachers (Jennett, Harris, & Mesibov, 2003). Burnout is considered to be a long-term stress reaction that particularly occurs among professionals working with people in the human service fields, that is, teachers, nurses, and social workers (Maslach & Schaufeli, 1993). Although various definitions of burnout exist, it is most commonly described as a psychological syndrome of emotional exhaustion, depersonalization, and reduced personal accomplishment (Maslach, 1993).
Because of the significant level of emotional needs, labor, and work required for a teacher compared with other professions (Chang, 2009), a variety of studies have been conducted on teacher burnout since the 1970s. According to Farber (1991), depending on the type of school and the method of assessment, between 5% and 20% of all American teachers are burned out. In addition, another 30% to 35% of all American teachers are strongly dissatisfied with the teaching profession. A study on a European sample (Boyle, Borg, Falzon, & Baglioni, 1995) also revealed that 60% to 70% of the teachers frequently experience stress and 30% of the teachers in this population suffer from burnout symptoms. International surveys conducted by International Labor Organization-United Nations Educational Scientific and Cultural Organization Joint Committee reported that the rates of teachers suffering from stress range from 23% to 33% (Macdonald, 1999). Teacher burnout has become a serious problem in Korea as well. Korean teachers believe they are treated disrespectfully by students and parents. More than 70% of teachers find nothing worthwhile about their job (Y. J. Kim, 2013).
Similar to burnout, depression is one of the psychological strains that represent a reaction to the stressor. Many dysphoric symptoms of burnout, including fatigue, withdrawal, irritability, difficulty relaxing when outside of work, and feelings of diminished enthusiasm, are also typical symptoms for depression (Bakker et al., 2000). As a result, questions about the conceptual overlap between burnout and depression have been continuously raised (Glass & McKnight, 1996; Taris, 2006). Results from studies examining the concepts of depression and burnout, based on manifestation, biomarkers, developmental trajectories, and statistical relationships (Ahola & Hakanen, 2007), revealed both similar and distinctive features between burnout and depression. However, a fundamental distinction between burnout and depression is that burnout is supposedly work related, whereas depression is expected to be more pervasive in nature and multifactorial in origin (Bakker et al., 2000). Specifically, even though burnout and depression are both reactions to the stressor, the stress contexts that can engender burnout and depression are different. Furthermore, according to a study by Hakanen, Schaufeli, and Ahola (2008), a model composed of two second-order factors--burnout and depression, respectively--demonstrated an acceptable fit, whereas a one-factor model combining burnout and depression illustrated a very poor fit. Thus, it seems fair to conclude that they are not completely redundant (Ahola & Hakanen, 2007).
To date, a variety of studies have tried to investigate the relationship between burnout and depression. Dominant research results assert that burnout is one of the strongest predictors of depression. For instance, in some cross-sectional settings, burnout has been shown to lead to depression among some occupational samples, such as teachers, health care workers, and hospital nurses (Bakker et al., 2000; Glass, McKnight, & Valdimarsdottir, 1993: Leiter & Durup, 1994). Another study indicated that burnout will result in depression, in particular, when it is accompanied by feelings of inferiority (Brenninkmeyer, Van Yperen, & Buunk, 2001). However, there are opposite results as well. Golembiewski, Lloyd, Scherb, and Munzenrider (1992) reported that depression has been depicted as a deteriorating factor on burnout. One explanation is that those who are depressed are likely to have little resources to meet the demands of their work, and, as a consequence, they are more vulnerable to become burned out (de Lange, Taris, Kompier, Houtman, & Bongers, 2004). Finally, reciprocal relationships between burnout and depression also have been reported. A recent study of a two-wave longitudinal setting in a sample of dentists (Ahola & Hakanen, 2007) indicated that burnout and depression are reciprocal; that is, occupational burnout predicted new cases of depressive symptoms, and depression predicted new cases of burnout. Also, Salmela-Aro, Savolainen, and Holopainen's (2009) study presented a reciprocal pattern of relationship between burnout and depression.
One possible explanation for these mixed findings is that the previously mentioned studies varied in their participants' occupation. Occupational stress occurs when job demands do not match the person's adaptive resources (Schaufeli & Buunk, 2003): therefore, research on different occupational samples are expected to result in varying relationships between burnout and depression (Ahola & Hakanen, 2007; Bakker et al., 2000; de Lange et al., 2004; Glass et al., 1993; Leiter & Durup, 1994). Thus. clarifying characteristics of the studied sample might be critical in future investigations. From a methodological perspective, however, the major limitation of previous studies is that they have been largely cross-sectional (Bakker et al., 2000: Leiter & Durup, 1994), making it impossible to infer causal relationships between burnout and depression. A scarcity of longitudinal studies has been reported, but the causal directionality between burnout and depression is not firm yet. Thus, the aim of this study is to investigate the causal relationship between burnout and depression among Korean teachers. As a preliminary analysis, the relationship between the rate of change in burnout and that of depression would be examined along with the relationship between the initial level of burnout and that of depression, using the latent growth modeling (LGM). Subsequently, an autoregressive cross-lagged modeling (ACLM) approach would be used to detect a causal relationship (J. H. Kim, Kim, & Hong, 2009) between burnout and depression.
The current study focused on middle and high school teacher burnout and depression. A total of 499 teachers participated in surveys to examine the longitudinal relationship between burnout and depression. Data were collected three times every 6 months. Of the participants, 77.2% (n = 378) were women and 22.8% (n = 111) were men. School distributions were as follows: 44.9% were middle school teachers, 6.1% were vocational high school teachers, and 49.0% were general high school teachers. The teachers had worked for an average of almost 10 years.
Maslach Burnout Inventory-Educator Survey (MBI-ES; Ruy, Park, & Yoon, 2003). The Korean version of the MBI-ES was used to measure teachers' burnout symptoms. The MBI-ES is specially used in identifying and assessing the levels of burnout of teachers and educational administrators. The survey consisted of 22 items related to the feelings a person might have as a result of being burned out. The rating scale ranged from 0 to 6:0 = never, 1 = a few times a year or less, 2 = once a month or less, 3 = a few times a month, 4 = once a week, 5 = a few times a week, and 6 = every day. The MBI-ES is composed of three subscales that evaluate the components of burnout: (a) Emotional Exhaustion (EE), chronic tiredness and physical and emotional depletion resulting from teaching and counseling a large number of students on a continual basis; (b) Depersonalization (DP), negative and indifferent attitude toward students; and (c) Personal Accomplishment (PA), contribution that a teacher makes for the intellectual and well-being advancement of students. Although low scores on the Depersonalization and Emotional Exhaustions subseales indicate a low degree of burnout, high scores on the Personal Accomplishment subscale also indicate a low degree of burnout. In the current study, scores of the Personal Accomplishment subscale were reversed and the name was modified to Reduced Personal Accomplishment.
Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977). The CES-D consisted of a 20-item self-report scale accompanied by a 4-point Likert scale, ranging from 0 (seldom or never [less than 1 day per week]) to 3 (most of the time or always [5 to 7 days per week]). Participants were asked to indicate how often they experienced feelings of worthlessness, hopelessness, loss of appetite, and sleep disturbance during the preceding week. The CES-D included four subseales, namely Somatic-Retarded Activity (seven items), Depressed Affect (five items), Positive Affect (four items), and Interpersonal Affect (two items). Items 9 and 13 are not included in any subscales, but do contribute to the depression score based on the total scale in practical applications. Scores were summed to a total score. Scores of more than 16 have been regarded as an indicator of severe depression (Radloff, 1977). Radloff (1977) reported an internal consistency reliability of .89.
We used LGM to examine (a) the patterns of c, hange in teacher burnout and depression, separately; (b) the consequenee of a change in teacher burnout on the ehange in depression; and (c) possible confounding influences on the relationship between teacher burnout and depression. Using AMOS (Version 18.0), the LGM analyses were conducted with full-information maximum likelihood estimation, to manage the missing data. On the basis of structural equation modeling, LGMs describe change over time in terms of latent intercepts and latent slopes, both of which can be treated as random variables differing between individuals. In Figure 1, the graphic illustration of the analyzed model can be seen. The data are mapped onto a measurement and structural model. The measurement model indicates individual growth in each construct and is defined by two growth parameters (intercept and slope). The intercept is the starting point of the trajectory. The slope coefficient is change per unit of time (in our analysis, three waves in 1.5 years); it can be described as linear, quadratic, and another alternative functional form. The measurement models also assume the intercept and slope variances, which is individual differences (heterogeneity) around the sample means. For example, the left side of the figure indicates the individual growth model for teacher burnout with two latent growth parameters, intercept and slope. The right side of the figure denotes the individual growth models for the outcome variables, teacher depression. The structural model, which defines the relationship between latent growth models for teacher burnout and depression, then permits an examination of the relationship of change in one variable on change in the other.
Chi-square is highly sensitive to sample size (Hu & Bentler, 1995); thus, we used multiple indices to estimate significance: chi-square, the root mean square error of approximation (RMSEA; Steiger, 1990), the comparative fit index (CFI; Bentler, 1990), and the Tucker-Lewis index (TLI). According to Hu and Bentler (1995), values of .95 or greater for TLI and values of .06 or smaller for RMSEA indicate good fit. When CFI is larger than .90, a model is regarded as reasonably fitting the data (Bentler, 1990).
For our research, we were concerned with a test of the relationship between the burnout and depression variables (see Figure 2). To examine the longitudinal relationships between academic burnout and depression, ACLM was used in this study. The main idea of the ACLM is that the score variation at Time T-1 explains the scores at Time T (Curran & Bollen, 2001). Thus, this model is adequate for longitudinal analysis of data, where the score at Time 1 explains the score at Time 2 and, in turn, the score at Time 2 explains the score at Time 3, and so on (Bast & Reitsma, 1997). Autoregressive coefficients are the parameters acquired by regressing the variable at each time onto the same variable at the previous time. Cross-lagged coefficients are parameters acquired by regressing one variable at Time T to the other variable at T-1, constraining for the autoregressive prediction of structure from itself at T-1 (Hong, You, & Wu, 2010). Metric invariance and path coefficient invariance across the waves are required in ACLM (M. Kim, Kim, & Kim, 2009). Thus, the current study tested the hierarchically restrictive assumptions continuously on a series of models. First, the model without any constraint was tested (Model 1). Second, we conducted the test of metric invariance by restraining the factor loadings to be equal across three waves (Model 2). Third, we performed the test of path coefficient invariance by restraining the path coefficients to be equal across waves (Model 3).
Table 1 depicts the means, standard deviations, and indices of normality assumption across the three waves for the measures of teacher burnout and depression. Reliability ranged from .82 to .87 across the three time waves, demonstrating the high levels of internal consistency. The mean of burnout increased from Time 1 to Time 2 and remained stable. The mean of depression increased from Time 1 to Time 3, continuously. The values of skewness for all variables were under 2 and the values of kurtosis were all under 7 (West, Finch, & Curran, 1995), indicating normal distribution. The correlation coefficients among the measures of teacher burnout and depression are provided in Table 2. All correlations were positively significant, showing moderate to strong correlation coefficients.
Next, we examined whether the model in which initial status (intercept) and change rate (slope) of teacher burnout were related with initial status (intercept) and change rate (slope) of teacher depression (see Figure 1). Most of the fit indices of the model were appropriate, [chi square] (9) = 29.30, p < .001, CFI = 0.96, TLI = 0.91, RMSEA = 0.07. Even though the value of chi-square is significant, because it is known to be highly sensitive to sample size and the distribution assumption (Hu & Bentler, 1995), other indices were considered together. Given all the other indices were acceptable, this model was retained.
There was a statistically significant and positive relationship ([beta] = .71, p < .001) between initial status of teacher burnout and depression. In addition, there was a statistically significant and positive relationship ([beta] = .74, p < .05) between change rate of teacher burnout and that of depression.
To examine the reciprocal relationship between teacher burnout and depression, ACLM was used. Before examining the causal relationships between burnout and depression, it is required to test a series of models by sequentially adding hierarchically restrictive invariance assumptions. Thus, three models are presented in Table 3. Model 1 is the baseline model, Model 2 is the metric invariance model, and Model 3 is the path coefficient invariance model. Each model was compared sequentially to find the best fitting model.
To explore the best fitting model, a chi-square difference test was conducted. In the first chi-square difference test, the fit of Model 2 was not significantly different from that of Model 1, [DELTA][chi square](8) = 11.15, p = .193. This result implies that all factor loadings operate equally across the three waves. In the second chi-square difference test, the fit of Model 3 was not statistically different from that of Model 2, [DELTA][chi square](4) = 9.13, p = .058, implying that the corresponding path coefficients were not different from each other across the three waves. Therefore, Model 3 was finally adopted, [chi square](118) = 257.01,p < .001, CFI = 0.95; TLI = 0.93, RMSEA = 0.05.
Given that the assumptions of metric invariance and path coefficient invariance were not violated, the causal relationships between burnout and depression were examined. In Table 4 and Figure 2, the path coefficient of teacher burnout from Time 1 to Time 2 was [beta] = .71 (p < .001) and from Time 2 to Time 3, it was [beta] = .83 (p < .001). In addition, the difference in the path coefficient of teacher depression from Time 1 to Time 2 ([beta] = .49, p < .001) and from Time 2 to Time 3 ([beta] = .56, p < .001) indicates that previous episodes of burnout and depression influence later ones. In addition, significant cross-lagged effects were found from burnout at Time 1 to depression at Time 2 ([beta] = .19, p < .05), and from burnout at Time 2 to depression at Time 3 ([beta] = .21, p < .05). The path coefficients from previous teacher burnout to depression at the next time were statistically significant. On the other hand, the path coefficients from previous depression to burnout at the next time were not statistically significant.
Burnout has been conceptually and empirically demonstrated to be positively related to psychological depression. To date, several researchers (Hakanen et al., 2008; Salmela-Aro et al., 2009) attempted to explore the causal relationships between burnout and depression. However, most of the previous studies have relied on cross-sectional design (Bakker et al., 2000; Leiter & Durup 1994), which limits the explanation of the causal relationships of burnout and depression. The findings were also mixed. Some researchers (Bakker et al., 2000; Glass et al., 1993; Leiter & Durup, 1994; Salmela-Aro et al., 2009) claimed that burnout causes depression, whereas other researchers (de Lange et al., 2004; Golembiewski et al., 1992) argued that depression leads to burnout. In addition, some researchers (Ahola & Hakanen, 2007) reported that burnout and depression have been found to be reciprocal relationships. Thus, it is necessary to identify how burnout and depression are related to each other using longitudinal designs.
The present study explored the developmental process in the structural relationship between burnout and depression of teachers using the LGM and ACLM methods. First, the study confirmed that initial status (intercept) and change factors (slope) for burnout were related to the initial status (intercept) and change factors (slope) for depression. Because change factors between burnout and depression are significantly related to each other, the ACLM was conducted to find casual sequences between burnout and depression. The results revealed that there were statistically significant cross-lag effects from burnout to depression. Specifically, both the path from burnout at Time 1 to depression at Time 2 and the path from burnout at Time 2 to depression at Time 3 were significant. In contrast, the effects of depression on burnout were not significant.
On the basis of the results of current research, it seems possible that burnout is an initial phase in the development of depression in the sample of teachers. These results support earlier findings with cross-sectional data on the general population (Ahola et al., 2006). For example, Ahola and Hakanen (2007) reported that "even though the relationship between burnout and depression is reciprocal, the path from burnout to depression appears to be stronger than the path from depression to burnout" (p. 109). In addition, Hakanen et al. (2008) reported that burnout predicted depression and not vice versa. Moreover, Salmela-Aro et al.'s (2009) study revealed that school burnout more strongly predicted subsequent depressive symptoms later on rather than vice versa. Because concepts overlap between burnout and depression, the extent to which burnout differs from depressive symtomatology has been questioned (Taris, 2006). However, considering the highly chronic nature of depression symptoms, burnout may in fact lead to depression rather than vice versa or the development may occur simultaneously (Hakanen et al., 2008). Mental health has been revealed to get worse as burnout advances (Golembiewski et al., 1992). Moreover, the more severe burnout is, the closer it resembles depression symptoms (Iacovides, Fountoulakis, Kaprinis, & Kaprinis, 2003; Salmela-Aro et al., 2009). Because burnout syndrome is a precursor of job-related depression, amelioration of this type of disorder may well need strategies that attend to those employment settings that give rise to burnout (Glass et al., 1993). In addition, perceived lack of control (Glass et al., 1993), job strain (Ahola & Hakanen, 2007), and loss of job status (Brenninkmeyer et al., 2001) related to burnout are all symptoms that affect depression.
Moreover, burnout prevention programs are important for teachers to avoid depression. School professionals (e.g., school principals and counselors) have to become aware of teachers' burnout for preemptive interventions to take place. Blau's (1964) social exchange theory explains that teachers frequently give more than they receive, which may eventually deplete teachers' emotional resources, and thus foster the development of the burnout syndrome (Farber, 1991). In addition, Leiter and Maslach (1988) argued that emotional exhaustion stems from an often unrealistic desire to solve the frequently intractable problems of students or clients. Indeed, the more idealistic the teacher, the greater the risk of burnout (Ashforth & Lee, 1997). Thus, knowledge or self-awareness of the most important sources of reciprocity (or lack thereof) and effective coping strategies may help teachers in preventing or alleviating burnout (Bakker et al., 2000).
Several researchers (Kyriacou, 2001; Maslach & Leiter, 1997) pointed out that teachers' burnout is a negative response to the imbalance between job demands and their resources. Korean teachers' demands and resources are similar to that of other teachers. For example, teaching-specific demands refer to organizational and interpersonal aspects of the teaching job, namely role ambiguity, time pressure, and student aggressiveness (Griva & Joekes, 2003; Ryu, 2002). Resources refer to control opportunities or social support. Job resources may increase job satisfaction and reduce the risk of experiencing burnout (Lee, Ju, & Lee, 2011; Skaalvik & Skaalvik, 2009). Teachers who suffer from burnout might not have adequate resources to meet the demands of their work, and this chronic situation might lead them to severe mental health problems such as depression. Therefore, it is necessary that school professionals evaluate the sources of demands as well as the resources of teachers at school.
Comprehensive evaluations such as screening tests for teacher burnout (including job demands and resources) could be useful to assess burnout in teachers. In such a demanding job, classroom overload and interpersonal conflicts and students' behavioral problems contribute to teacher burnout (Hakanen, Bakker, & Schaufeli, 2006). Teachers' perception of both motivation and self-efficacy are important factors reducing burnout (Brouwers & Tomic, 2000; Skaalvik & Skaalvik, 2009). After screening tests, interventions to reduce demands or to increase resources should be conducted to alleviate burnout symptoms. For example, a mentoring or coaching program could encourage teachers to reflect on their practices, such as work organization and classroom management. Also, supportive behaviors of the school principal could foster feelings of self-efficacy in teachers (Fernet, Guay, Senecal, & Austin, 2010). These active interventions may support teachers' feelings of competence in the classroom.
The present study has several limitations. First, moderate variables were not integrated in this study. The process of burnout and depression has also been established to be multidimensional and correlating to various demographic and contextual variables. For instance, Hakanen et al. (2008) claimed that job demands predicted burnout over time, which in turn predicted future depression. In addition, work-related conditions, such as financial strain or work--family conflict, might be individual-specific factors influencing burnout as well as depression (Middeldorp, Cath, & Boomsma, 2006). Brenninkmeyer et al. (2001) claimed that individuals with high levels of burnout and a reduced sense of superiority seem to develop depression. Therefore, further study containing moderate variables (e.g., demographic variables, work characteristics, personality) is needed to provide more detailed suggestions for intervention. Second, the cross-lagged effect was relatively small, and thus the results must be generalized with caution. Hence, further study containing a bigger sample and a follow-up study is needed to generalize the process of burnout and depression. Third, the study sample focused on Korean teachers in middle and high schools. There is a limitation to generalize the results of our study for all occupational groups. Therefore, further study containing various occupational samples is essential in generalizing the results. Fourth, data were collected from a heterogeneous sample. Data were gathered from a teacher training program, where the teachers from all areas of South Korea were meeting. Thus, further study containing a homogeneous sample is needed for more precise results. Fifth, we selected the CES-D instrument that has been the most widely used in studies of depression, because it is convenient to use in most settings (Santor, Zuroff, Ramsay, Cervantes, & Palacios, 1995); however, the CES-D (Radloff, 1977) is somewhat outdated. Finally, data for this study were collected three times every 6 months for 1.5 years. However, this might not have been long enough to track the progression of burnout and depression, which may take much longer to develop over a teacher's career. Future research in this area should include longitudinal designs to capture changes over longer periods of time.
In conclusion, despite several limitations, our findings increase the understanding of teachers' mental health. In occupational health care, burnout is a serious alarm signal of an unfavorably developing working situation (Ahola & Hakanen, 2007). Teacher burnout should be taken seriously because it can proceed to depression. Early evaluation and intervention concerning a teacher's work-related burnout should be considered to prevent chronic mental health problems.
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Hyojung Shin, Hyunkyung Noh, Yoojin dang, Yang Min Park, and Sang Min Lee, Department of Education, Korea University, Seoul, South Korea. Correspondence concerning this article should be addressed to Sang Min Lee, Department of Education, Korea University, Anam-Dong, Sungbuk-Gu, Seoul, South Korea (e-mail: email@example.com).
TABLE 1 Reliabilities and Descriptive Statistics Variable [alpha] M SD Skewness Kurtosis Burnout Time 1 0.86 3.10 0.81 0.18 -0.05 Time 2 0.85 3.14 0.88 0.12 -0.41 Time 3 0.84 3.13 0.81 -0.05 -0.49 Depression Time 1 0.87 1.58 0.42 1.21 2.00 Time 2 0.87 1.61 0.45 1.13 1.51 Time 3 0.86 1.67 0.44 0.65 -0.08 TABLE 2 Cross-Sectional and Longitudinal Correlations Between Burnout Factors Time 1 Time 2 Time 3 Variable 1 2 1 2 1 2 Time 1 1. TB -- 2. DP 0.64 Time 2 1. TB 0.61 0.46 -- 2. DP 0.42 0.55 0.68 -- Time 3 1. TB 0.68 0.50 0.82 0.64 -- 2. DP 0.45 0.50 0.59 0.61 0.65 -- Note. TB = Teacher Burnout; DP = Depression. All variables are significant at p < .001. TABLE 3 Summary of Model Fit Statistics in Invariance Tests Model (M) [chi square] df TLI CFI RMSEA MC 1 236.73 106 0.92 0.95 0.05 2 247.88 114 0.93 0.95 0.05 M1 vs. M2 3 257.01 118 0.93 0.95 0.05 M2 vs. M3 [DELTA] [DELTA] Model (M) [chi square] df p 1 2 11.15 8 0.193 3 9.13 4 0.058 Note. TLI = Tucker-Lewis index; CFI = comparative fit index; RMSEA = root mean square error of approximation; MC = model comparison; Model 1 = baseline; Model 2 = metric invariance; Model 3 = path coefficient invariance. TABLE 4 Path Coefficients of Autoregressive Cross-Lagged Modeling Path Times 1-2 Times 2-3 Teacher Burnout [right arrow] Teacher Burnout .707 *** .826 *** Depression [right arrow] Depression .489 *** .555 *** Teacher Burnout [right arrow] Depression .187 *** .211 * * p < .05. *** p < .001.
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|Author:||Shin, Hyojung; Noh, Hyunkyung; Jang, Yoojin; Park, Yang Min; Lee, Sang Min|
|Publication:||Journal of Employment Counseling|
|Article Type:||Clinical report|
|Date:||Sep 1, 2013|
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