Determinants of occupational stress among urban Indian school teachers

Abstract

Occupational stress among teachers has become a matter of increasing concern. It is not only evidenced from the large body of studies on the subject but also through factors that predispose educators to work related stress. The present study also tends to identify demographic patterns of workplace stress as well as examine the role of correlates like socio-economic status and work experience on stress levels. It further explores specific workplace stressors reported by Indian teachers. The sample consisted of 398 teachers, 31.9% males and 68.1% females, from urban centre, New Delhi, India. The measures included the National Stress Awareness Day Stress Questionnaire and a self-report questionnaire designed for the purpose of the research. Results showed that with 52% public and rest private school employees, stress levels were found to be high among 28% (111 participants) of the sample. All demographic variables, Socio Economic Status (SES) categories, promotional and experience factors were contributing significantly to the stress prediction model but age group, work experience and promotion opportunities were reported as prime explanatory variables for the model (B = 2 approx.; p < 0.05). For every unit/category change in these variables, the stress score was seen to increase by 2. The optimal stress score that culminates to larger stressor of affecting general health was 10.8 and was 88% sensitive while a stress score beyond 11.5 is seen to affect work deliverables to students as reported by teachers. Findings were also reviewed in the context of practical implications they may render with probable reasoning. We recommend a constant evaluation of stress levels, for teachers, and providing appropriate counselling may be the stepping stone to reduce stress and improve quality of life for the teachers.

Keywords

Teacher stress school New Delhi National Stress Awareness Day stress levels

Introduction

Defining occupational stress and ‘teacher stress’ has been a challenging task and even more demanding is the conception of quantification of ‘teacher stress’. There are many definitions of stress and approaches to research on stress (Kyriacou, 1987, 2001; McGrath, 1977). Conceptualisation of teaching stress is derived from various elements of its environment, on individual differences in perception and appraisal of situations, and by concentrating on the individual’s stress response (Kyriacou and Sutcliffe, 1977; Travers and Cooper, 1996). The notion of teacher stress must be explored through all aspects of stress including being student teachers, probationary teachers, teachers on verge of retirement, primary and secondary teachers (Lackritz, 2004) and issues arising from dealing with authority, for example the problems facing heads of departments and headmasters (Dunham, 2002; Lacey, 2012).

Workplace stress in teachers

The increasing rates of workplace stress and burnout are posing a significant challenge to well-being in the working environment globally (Colligan and Higgins, 2006; Milczarek et al., 2009; Shkëmbi et al., 2015), as observed in European (Milczarek et al., 2009) and other countries like India where 88% of higher secondary teachers were found to be experiencing moderate to high levels of occupational stress (Reddy and Anuradha, 2013). Lau et al. (2005) and Shkëmbi et al. (2015) reviewed workplace stress and documented that it was highest in Greece (55%) and Slovenia (38%), whereas the lowest levels were reported in the United Kingdom (12%) and Germany (16%). In Hong Kong, the recent figures, compared with those of five years ago, indicated that 91.6 and 97.3% of the responding teachers have reported an increase in perceived stress level, respectively (Chan et al., 2010; Lau et al., 2005; Shkëmbi et al., 2015). Milczarek and co-workers mentioned that European Agency for Safety and Health at Work (2009) projected a report that stress was most common in education and health-related professions (nearly 28.5%) in comparison to other job sectors (Milczarek et al., 2009; Shkëmbi et al., 2015). Nearly two decades ago, in United Kingdom, National Union of Teachers found that 36% of British teachers reported stress most of the time whereas 41.5% of these teachers reported high levels of stress (Shkëmbi et al., 2015; Smith et al., 2000). Stress rates are known to vary greatly from country to country as in Turkey/Macedonia, they were documented to be mild to moderate (Eres and Atanasoska, 2011) but were found to be challengingly high in Slovenia and India and were found to be frustrating and emotionally depleting (Hanushek, 2007; Lambert and McCarthy, 2006; Reddy and Anuradha, 2013; Shkëmbi et al., 2015).

Determinants of workplace stress for teachers

Occupational stress among teachers has become a matter of increasing concern not only as evidenced from the large body of studies on the subject (Kyriacou and Sutcliffe, 1977) or as factors that predispose educators to work-related stress but also has been documented by the growing demand for enrolment in programmes and workshops developed to help teachers cope with the problem and reduce occupational stress (Borg et al., 1991). Research on stress-related factors in the workplace has focused on the role of variables such as age, gender, work experience, marital status and other demographic factors especially in Albania (Shkëmbi et al., 2015). Research focused on prognosticators of stress among Australian educators revealed that the greatest sources of stress were time and workload pressures, which were related to being female; permanently employed; committed to teaching; and seeing oneself as conscientious, shy and unhappy (O’Connor and Clarke, 1990). Italian teachers portrayed that significant potential sources of stress were linked to local management of the school changes in the curriculum and the appraisal of teaching (Zurlo et al., 2007). Hong Kong teachers report heavy workload, time pressure, education reforms, external school review, pursuing further education and managing students’ behaviour and learning as the most frequently reported sources of work stress (Chan et al., 2010). In India, the reporting about stressors for teachers has been scarce. High school teachers from Vellore reported that gender, type of school the teachers are working in and years of experience, age, community, marital status, educational qualification, nature of the subjects the teachers are teaching, salary received by the teachers and location of the school were tested to be significant predictors of occupational stress (Reddy and Anuradha, 2013).

The review of literature of most of these studies revealed that the phenomenon of ‘stress problem’ of teachers was widespread and was not restricted to a particular country (Abel and Sewell, 1999; Borg and Riding, 1991; Boyle et al., 1995; Fontana and Abouserie, 1993; Solman and Feld, 1989). In Hong Kong, as it has been reported that public awareness of the work stress problem of teachers had been heightened with the increasing occurrence of suicide cases amongst teachers (Chan et al., 2010; Lo, 2003). The health of teachers is seriously affected by stress (Wiley, 2000) and may also adversely affect their students and the learning environment (Chan et al., 2010; Chan and Hui, 1995). In addition, stress problems of teacher might cause an increase in teaching costs (Chan et al., 2010). Given these crucial changes and considering their potentially negative role (Chan, 2003) with respect to the stress experienced by teachers globally, the aim of the present study is to analyse stress in an Urban Indian school setting. The purpose of the present study was to investigate workplace stress and stress-related variables among teachers in schools of New Delhi, India. The study also tends to identify demographic patterns of workplace stress as well as examine the role of correlates like socio-economic status and work experience on stress levels and to further explore specific workplace stressors reported by Indian teachers.

Materials and methods

The study assessed the relationship between different aspects of work stress and (a) measures of demographic factors, (b) work and prospect variables and (c) prime stimuli of stresses.

Data collection

Data were collected by means of mailed questionnaires which were forwarded to 425 teachers enrolled in education courses from various public and private schools in the urban city of New Delhi, India. Of the questionnaires, which were returned, 398 were usable, giving a response rate of 94%. Sample size accounted for alpha and beta errors at 5 and 20% with 10% added to sample for non-response error. Respondents did not differ from non-respondents on any of the measures on which they were compared. Based on gender, 31.9% of the sample were males and 68.21% females; based on place of teaching, 52% of participants came from public schools and 48% from private schools. The age range of participants was from 20 to 65 years. The work experience reported by the teachers varied from 0 to more than 10 years. Further characteristics of the sample are provided in Table 1. Participants were ensured anonymity and confidentiality of their reports and provided informed consent to participate in the research. Part-time educators; university teachers; educational institutions other than registered/recognised schools; administrative staff including principals, deans and headmistresses were excluded.

Table 1.

Sample characteristics.

Variables		N	Valid percentage
Type of school	Public	207	52.0
Type of school	Private	191	48.0
Gender	Male	127	31.9
Gender	Female	271	68.1
Marital status	Married	210	52.8
	Single	78	19.6
	Divorced/widowed	89	22.4
	Others/no answer	21	5.3
Age group	20–30 years	132	33.2
	31–40 years	92	23.1
	41–50 years	76	19.1
	51–60 years	55	13.8
	Above 61 years	43	10.8
Teaching qualification	Yes	315	79.1
Teaching qualification	No	83	20.9
Years of work experience	<5 years	87	21.9
	5–10 years	167	42.0
	>10 years	144	36.2
Educational qualification	Undergraduate	83	20.9
	Master	224	56.3
	Doctorate	86	21.6
	Others/no answer	5	1.3
Promotion opportunities	Excellent	28	7.00
	Good	100	25.00
	Fair	127	32.00
	Poor	143	36.00
Socio-economic status	Lower middle	42 (mean score 43.2)	10.6
	Upper middle	52 (mean score 52.9)	13.1
	High	220 (mean score 70.7)	55.3
	Upper high	84 (mean score 78.3)	21.1

Research instrument–questionnaire

The first part of the questionnaire assessed demographic and socio-economic factors. Respondents were asked to report age, gender, level of education, work experience, marital status, and type of school, and other variables of interest in the study. Socio-economic status scale (Aggarwal et al., 2005) was employed to assess the Socio Economic Status (SES) of the family. Promotional prospects were rated as poor, fair, good or excellent.

Stress measures included the National Stress Awareness Day (NSAD) Stress Questionnaire (ISMA, 2012), and one self-report questionnaire modified by the authors for the purpose of the research after assessing focused group discussions and a previous study (Shkëmbi et al., 2015). The NSAD Stress Questionnaire assessed levels of workplace stress and included 25 questions, with Yes/No answers (ISMA, 2012). Internal consistency, α was found to be 0.89. Results are interpreted as follows:

4 points or less – You are least likely to suffer from stress-related illness;

5–13 points – You are more likely to experience stress-related ill health either mental, physical or both;

14 points or more – You are the most prone to stress showing a great many traits or characteristics that are creating unhealthy behaviours.

Total scores were derived by counting the number of items reported to be true for them. NSAD suggests that a Yes answer score should be counted as 1 (one) and a No answer score as 0 (zero). As previously reported, scores up to 4 are considered as low levels of stress, scores in the range 5–13 as moderate levels of stress and scores above 14 are considered as high levels of stress (Shkëmbi et al., 2015). An additional measure was constructed for the specific purpose of the study and was based on existing research on workplace stressors and selected post FGDs. It included 12 items assessing stressors such as work overload, student discipline, work environment, lack of authority, inadequate wages, personal problems and work other than teaching. Each stressor was evaluated for its impact on a Likert scale from 1 (not at all stressful) to 5 (extremely stressful). The scores were reversed for negative statements, if any. Each respondent was then asked to choose one as the most essential stressor for their occupational stress. The respondents were also asked to respond to the following questions: ‘Does this occupational stress affects your general health’ and ‘Does it transpire into affecting work delivery towards students?’ These open-ended queries were then coded into Yes or No answers.

Statistical analysis

Chi-square tests were used for covariate analysis of all nominal predictors and once found to significantly affecting stress levels, they were selected as explanatory variables for subsequent regression analysis. Multiple standard regression analysis was used to predict the level of stress from a model of variables including type of school, gender, marital status, age group, teaching qualification, years of work experience, educational qualification, promotion opportunities and socio-economic status (Table 3). For the purpose of this analysis, SES score and stress levels were considered as continuous variables, while others were recoded as multichotomies or dichotomous variable. The plot of sensitivity versus 1-specifity is called receiver operating characteristic (ROC) curve and the area under the curve (AUC) is an effective measure of accuracy which has been considered with a meaningful interpretation (Hanley and McNeil, 1982). This curve plays a central role in finding the optimal cut-off values (Hajian-Tilaki, 2013) and in the present study we evaluate the same understanding that particular point in stress scores where it tends to affect general health and work delivery towards students. The cut-off was then employed to divide the sample into two groups < 10.8 and > 10.8 for stress scores. Logistic regression is a part of model building which is variation of the regression model, wherein the dependent variable is a categorical variable, stress scores < 10.8 and > 10.8. The logistic regression also allows the independent variables to be categorical variables like stimuli stressors of the given study. The logistic regression is nothing but the non-linear transformation of linear regression (Harrell, 2015). Binary logistic regression where the dependent variable is a dichotomous variable like high versus low stress scores was used. The regression value ranges between 0 and 1 and model fit is tested using the per cent correct prediction which is calculated based on the estimated p value (event occurring). The bigger the per cent correct predictions, the better the mode. Two-sided p-values < 0.05 were considered significant. All analyses were performed in IBM SPSS 23.0. Graphs were made in MS Excel.

Results

Table 2 presents chi-square test to determine all demographic variables, SES categories, promotional and experience factors associated with three categories of stress. With 398 respondents, it was found that teachers in private school, females, age group and being married reported highest stress level. A significant chi-square was reported for all explanatory variables like type of school, gender, marital status, age group, teaching qualification, years of work experience, educational qualification, promotion opportunities and socio-economic status at p < 0.001 providing evidence that indicator variables are all significantly associated with ≥ 14 points in stress score. Figure 1 illustrates the Pareto frequency analyses for each stressor reported. Work overload, additional administrative work and insufficient wages were the top most stimuli stressors for ‘teacher stress’ in the present sample.

Table 2.

Cross tabulation and test of association for demographic variable, SES categories, promotional and experience factors associated with three categories of stress.

Variables		NSAD scores (N)			Chi-square Significance
Variables		≤ 4 points	5–13 points	≥ 14 points	Chi-square Significance
Type of school	Public	81	49	77	p < 0.001
Type of school	Private	97	60	34	p < 0.001
Gender	Male	78	19	30	p < 0.001
Gender	Female	76	114	81	p < 0.001
Marital status	Married	44	78	88	p < 0.001
	Single	34	27	17
	Divorced/widowed	35	21	33
	Others/no answer	7	11	3
Age group	20–30 years	54	57	21	p < 0.001
	31–40 years	37	33	22
	41–50 years	43	14	19
	51–60 years	11	17	27
	Above 61 years	9	12	22
Teaching qualification	Yes	148	123	44	p < 0.001
Teaching qualification	No	6	10	67	p < 0.001
Years of work experience	<5 years	50	20	17	p < 0.001
	5–10 years	47	74	46
	> 10 years	57	39	48
Educational qualification	Undergraduate	34	38	11	p < 0.001
	Master	98	70	56
	Doctorate	22	24	40
	Others/no answer	0	1	4
Promotion opportunities	Excellent	10	14	4	p < 0.001
	Good	85	10	5
	Fair	37	40	50
	Poor	22	69	52
Socio-economic status	Lower middle	4	15	23	p < 0.001
	Upper middle	2	16	34
	High	98	78	44
	Upper high	50	24	10

NSAD: National Stress Awareness Day.

Figure 1.

Pareto frequency analyses for each stressor reported.

Table 3.

Summary for multiple regression model for stress scores with demographic factors and personal attributes.

Variables	Unstandardised coefficient	S.E.	Sig.
Type of school	0.804	0.333	0.016
Gender	−0.333	0.126	0.008
Marital status	−0.803	0.181	0.048
Age group	2.498	0.251	0.047
Teaching qualification	0.509	0.235	0.003
Years of work experience	2.082	0.049	0.007
Educational qualification	1.215	0.18	0.006
Promotion opportunities	2.199	0.533	0.009
Socio-economic status	−0.767	0.159	0.009
Constant	8.494	2.314	0

Adjusted R-square = 0.454; classification prediction = 94.2%.

Table 3 summarises the multiple regression model for stress scores with demographic factors and personal attributes. The model reached statistical significance, p < 0.001, and explained 45.5% of the variance in stress score. All variables were contributing significantly to the model where age group, work experience and promotion opportunities were reported as prime explanatory variables for the model (B = 2 approx.; p < 0.05). For every unit/category change in these variables, the stress score was seen to increase by 2.

The ROC curves illustrate the sensitivity of the optimal cut-off point to detect effects on general health and work delivery (Figure 2). AUC scores are shown in Table 4. The optimal stress score that culminates to larger stressor of affecting general health was 10.8 and was 88% sensitive while a stress score beyond 11.5 is seen to affect work deliverables to students as reported by teachers. This is a screening cut-off beyond which action may be recommended. It is crucial in a country with diverse occupations and ethnic background; a quick screening decision can be made on a single number for concluding or implying any clinical implications.

Figure 2.

Receiver operating curves for estimation of stress level score affecting health and work. (a) Curve I: Stress scores versus effect on general health and (b) Curve II: stress scores versus effect on student education delivery. ROC: receiver operating curve.

Table 4.

ROC estimates of stress level score affecting health and work.

Stress scores	Optimal value	Area	Sig.	Confidence interval
Stress scores	Optimal value	Area	Sig.	Lower bound	Upper bound	Sensitivity (%)	Specificity (%)
Effect on general health	10.8	0.63	p < 0.001	0.57	0.68	87.90	74.40
Effect on student education delivery	11.5	0.69	p < 0.001	0.63	0.75	91.50	68.00

ROC: receiver operating curve.

The bivariate logistic analysis (Table 5) shows that absence of authority, job insecurity, being overworked, student issues, hostile work environment and additional responsibilities (administrative or otherwise) exhibit a statistically significant effect on stress levels > 10.8 and after controlling for other stressors. The model highlighted high job demands, insufficient wages and lack of authority or respect as significant predictors for stress. There are 5.36 times higher chances that the stress levels will be high if one reports insufficient wages as a prime stressor whereas additional administrative work increases these adjusted odds to 5.6 (p < 0.01) to understand multivariate system.

Table 5.

Summary of logistic regression for high stress level scores (> 10.8).

Factors	Estimates			95% CI for Exp (B)
Factors	B	Sig.	Exp (B)	Lower	Upper
Absence of authority/power/control	2.62	0.06	13.71	2.85	21.10
Lack of respect from others	−0.27	0.58	0.76	0.30	1.96
Occupational insecurity	1.33	0.01	2.72	1.16	3.36
Work overload	2.44	0.04	3.64	2.08	4.99
Personal problems	0.31	0.70	1.36	0.28	6.49
Issues associated with students	−1.97	0.04	0.14	0.02	0.87
Unsatisfactory relationship with colleagues	−0.40	0.77	0.67	0.05	9.59
Lack of respect from supervisors	6.99	0.00	2.90	2.00	3.94
Additional administrative work	1.73	0.03	5.66	1.22	26.23
Additional/sudden responsibilities	−6.54	0.01	0.61	0.00	0.97
Physical working environment	−0.69	0.40	0.50	0.10	2.51
Insufficient wages	1.68	0.00	5.36	1.97	14.61
Constant	−15.322	0.089	0.000

Nagelkerke R-square = 0.84; prediction percentage = 96.2; omnibus test = p < 0.001.

Discussion

The most credible definition of ‘teacher stress’ was defined as

a response by a teacher of negative affect (such as anger, anxiety or depression) accompanied by potentially pathogenic physiological changes (such as increased heart rate, or release of adrenocorticotrophic hormone into the bloodstream) as a result of the demands made upon the teacher in his role as a teacher. (Kyriacou and Sutcliffe, 1977)

The overall level of ‘teacher stress’ reported suggests that this might be a noteworthy issue among teachers of Urban India. About a third (28%) of the teachers in the sample reported that they experienced a high level of job stress based on NSAD score > 14. This stress occurs more frequently in response to general stimuli like the regular hassles of managing with the amount of work, factors related to student, concerns with administration and work environment other than personal problems and physical environment within the organisation of the school, or matters within the employing system or the broader community. Similar factors have been reported earlier in an Australian study (O’Connor and Clarke, 1990; Troman, 2000).

In the present study, we also investigate explanatory factors among teachers and delineate the exact cut-off, beyond which stress transpires into general health and work deliverables to students. Certainly, the extensive nature of the problem and its focus in one area of the job, stress appears to be due to the nature of the job rather than a reflection of the characteristics of those individuals who are being recruited into teaching. Contrary to popular belief, in India, we found that teachers at public schools reported higher levels of stress as compared to private school counterparts. This may be attributed to the changing political scenario and tightening noose of government agencies over educational institutions to deliver the best (Kapur and Crowley, 2008). Reformatting conventional stereotypes about ‘teacher stress’, the study reported that being a married female puts one at an increased risk of stress (Cooper and Kelly, 1993; Travers and Cooper, 1996) due to strain from family roles and work-role expectations (Cooke and Rousseau, 1984).

Lack of a teaching qualification and low education levels might make it more difficult to successfully manage some challenges of the teaching role and hence both these factors have emerged as major determinants of stress in the present study. Others studies have claimed that lower levels of education were related to higher stress (Shkëmbi et al., 2015) in Nepal (Mondal et al., 2011) but it remains uncontested in other reports (Blaug et al., 2007). Increasing age was correlated to higher stress unlike other studies; other researchers documented that young teachers are more stressed due to lack of experience (Chan et al., 2010; Lau et al., 2005; Shkëmbi et al., 2015). Our findings were consistent with other studies that presented years of work experience as an essential predictor of stress for teachers (Jepson and Forrest, 2006; Schwarzer and Hallum, 2008). The dynamism that is associated with the profession, it is possible that the older teachers find it more difficult to adapt to change and new modes of education or responsibilities attached to their roles. Shkëmbi et al. (2015) documented that as older age and experience go together, older teachers might think they deserve higher wages due to their greater work experience.

It is essential to mention that the most popular stressors as reported by urban teachers were absence of authority/power/control, occupational insecurity, work overload, additional administrative work and insufficient wages. These results may have further implications for evidence-based research policies in India, which should aim towards improving performance by reducing levels of stress and improving overall quality of life among teachers. Also, beyond a certain level of stress, teachers believe that it affects their working with students and their general health. This phenomenon isn’t unheard of (Grayson and Alvarez, 2008; Guglielmi and Tatrow, 1998; Jennings and Greenberg, 2009).

The demands being put on many teachers are greater than their capacity to meet those demands. Multiple studies have reported that stress arises from a discrepancy between the external demands placed on teachers to complete the essential tasks of teaching, class preparation and marking and the sources available to them in terms of the time available to complete these tasks as derived from the Otto framework (Howard and Johnson, 2004; Otto, 1986; Sarros and Sarros, 1992). The implications are clear and may even be defined in terms of commitment which is also aspirational in nature. Low levels of stress were reported for teachers with higher chances of promotions (Gillespie et al., 2001; Johnson et al., 2005). One way to reduce teacher stress is to reduce the overall work requirements, contest their commitments and aspirations to job goals and/or extend the limited time frames available to meet the many demands of the job.

Implications of the study

Two models were constructed on specific demographic characteristics (e.g. marital status, type of school, gender) and personal attributes (work experience and promotion opportunities) which we know have more practical rather than theoretical relevance. It makes decoding stress patterns a lot simpler in terms of gender, age, marital status or work experience because such investigation would allow the identification of specific target group for intervention as suggested by Kosovian study earlier (Shkëmbi et al., 2015). The consideration of specific target group characteristics would subsequently guide the design of tailored and targeted interventions towards stress management for specific groups like advanced age, females, married teachers. The study also identifies a cut-off score > 10.8 and prime stressors for this occupation (teaching) which might feed into current administrative rules and other guidelines leading to change in current policy to account for improved deliverables. A limitation of this study may be lack of assessment of context-specific stressors in rural areas for we expect their particular challenges to be different from those in urban areas thereby compromising generalisability of the study in India. Another drawback of the present research is its undivided attention to determinants and not all the consequences of ‘teacher stress’.

Conclusions

The levels of stress experienced by urban Indian teachers present a complex web of personal and occupational factors. Besides personal and demographic attributes like age, marital status, the type of school one works in, it is also crucial to tabulate their years of work experience and the aspirations attached to the current profile. Nonetheless, we suggest there are multifaceted relationships between different types of stressors and these personal characteristics. It is time to implore the public and private institutions to revaluate their polices. A constant evaluation of stress levels, for teachers, and providing appropriate counselling may be the stepping stone to reduce stress and improve quality of life for the teachers.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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