Exploring resilience and mindfulness as preventative factors for psychological distress burnout and secondary traumatic stress among human service professionals

Abstract

BACKGROUND: Human service professionals are concerned with the intervention and empowerment of vulnerable social populations. The human service industry is laden with employment-related stressors and emotionally demanding interactions, which can lead to deleterious effects, such as burnout and secondary traumatic stress. Little attention has been given to developing knowledge of what might enable human service workers to persist and thrive. Cultivating and sustaining resilience can buffer the impact of occupational stressors on human service professionals. One of the psychological factors associated with cultivating resilience is mindfulness.

OBJECTIVE: The aim of this current research is to improve our understanding of the relationship between resilience, mindfulness, burnout, secondary traumatic stress, and psychological distress among human service professionals.

METHODS: The current study surveyed 133 human service professionals working in the fields of psychology, social work, counseling, youth and foster care work to explore the predictive relationship between resilience, mindfulness, and psychological distress.

RESULTS: The results showed that higher levels of resilience were a significant predictor of lower levels of psychological distress, burnout and secondary traumatic stress. In addition, higher levels of mindfulness were a significant predictor of lower levels of psychological distress and burnout.

CONCLUSIONS: The findings suggest that cultivating resilience and mindfulness in human service professionals may assist in preventing psychological distress burnout and secondary traumatic stress. Limitations of this study are discussed together with implications for future research.

Keywords

Work stress mental health healthcare workers

1 Introduction

Human service professionals who work to provide intervention and empower the most vulnerable, distressed, and disadvantaged people are routinely confronted with the psychological distress, emotional pain, and traumatic recollections of the individuals with whom they work [1]. The level of stress that human service professionals experience from these frequent and emotionally charged interactions can lead to psychological distress, burnout, and secondary traumatic stress [2].

1.1 Psychological distress, burnout and secondary traumatic stress

Psychological distress is largely defined as a state of emotional suffering, characterised by symptoms of anxiety (i.e., feeling tense, nervousness) and depression (i.e., sadness, hopelessness) [3]. Burnout refers to a multifaceted work-related disorder of three dimensions, which include; emotional exhaustion, depersonalisation, and reduced personal accomplishment [4, 5]. These dimensions describe feelings of being over-extended, fatigued, and depleted; attitudes of negativity and cynicism towards clients or work; and a reduced sense of efficacy and accomplishment [6, 7]. Secondary traumatic stress refers to work-related, secondary exposure to traumatic events (i.e., by listening to a client’s traumatic experience in session) and can produce emotional and physical reactions, such as fear, sleep difficulties, intrusive images, and avoidance of reminders of the client’s traumatic experiences [8, 9]. Human service professionals working with traumatised clients can experience stress, which can be a costly and significant source of mental health problems and psychological distress [10, 11].

Human service professionals comprise of diverse professionals including psychologists, social workers, counselors, youth, and foster care workers. In recent decades, professional quality of life for human service professionals has been a topic of growing interest [7]. Risk and protective factors of mental ill health have been researched, with potential protective factors including resilience and mindfulness [9]. Mental ill health is often considered an umbrella term that encompasses a continuum from mild to severe symptomology. The term mental ill health includes psychological distress, burnout, and secondary traumatic stress and is distinct from mental illness that consists of diagnosable disorders such as depression, bipolar disorder, anxiety, or schizophrenia.

In addition to individual effects on mental health and psychological well-being, the organisational consequences of burnout among human service professionals include; increased turnover and absenteeism, unproductive work behaviours, and reduced job-satisfaction [12]. However, these negative outcomes not only effect the organisation but also affect the human services professional’s ability to effectively care for others [13]. Thus, practitioner-focused research has recognised the importance of building resilience in the promotion of psychological well-being and in the preservation of service standards [14].

1.2 Resilience

Resilience has been viewed as a “buffer” which protects individuals from adverse environmental influences and forces [15]. Although a universal definition does not exist, resilience considers an individual’s capacity to overcome adversities that would otherwise be expected to have negative consequences [16]. Individuals with high levels of resilience exhibited faster physiological and emotional recovery from stressful life events (i.e., heavy caseloads and stressful work conditions) [17]. In a qualitative study of the phenomenon among Australian mental health clinicians, Edward [18] aptly described resilience as “the ability to bounce back from adversity, persevere through difficult times, and return to a state of internal equilibrium” [p. 143]. Thus, much research has been directed towards what factors are most effective in buffering against stress and one factor that seems to be most important on the basis of this research is resilience [19].

1.3 Mindfulness

Of the psychological factors thought to contribute to resilience, mindfulness has increasingly gained attention in recent years [20, 21]. Conceptualised as an intentional state of awareness, mindfulness concerns the process of bringing one’s attention to the present moment, in a non-judgmental and accepting manner [22]. Through the practice of various techniques, individuals are able to cultivate a state of mindfulness and develop a number of skills considered to be of transdiagnostic importance [23]. For example, increased mindfulness has been shown to be correlated positively with several aspects of psychological well-being, and negatively associated with burnout and secondary traumatic stress [24]. Research investigating the beneficial effects of increased levels of mindfulness has reported improvements in distress tolerance [25], emotion regulation skills [26], and psychological flexibility [27]. Furthermore, Shapiro [9] argues that mindfulness shows promise as a protective factor against burnout and secondary traumatic stress, as mindfulness is associated with a greater ability and willingness to tolerate and accept negative emotions. This notion is supported with results showing that after attending a brief four-week mindfulness intervention health professionals reported significant reduction in symptoms of burnout and improved life-satisfaction [28].

1.4 Age and gender

The research is unclear on the impact that individual factors, such as age and gender, have on burnout [29]. For example, Ackerley et al.’s [30] study showed that while age was associated with burnout among psychologists, with younger psychologists experiencing more emotional exhaustion than older psychologists, relationship status, and gender were not. In contrast, Bearse et al.’s [29] study involving 260 psychologists found that females reported significantly greater levels of vicarious traumatisation and compassion fatigue compared to males. Furthermore, Sangganjanavanich and Balkin’s [31] study of 220 counselor educators indicated that neither age nor gender had a significant impact on levels of burnout. Due to the inconsistent findings to date, this current study examined the relationship between age, gender, and burnout.

1.5 Aim of study

The current study explored the predictive relationship between resilience, mindfulness, psychological distress, burnout, and secondary traumatic stress among human service professionals. High levels of mindfulness and resilience were hypothesised to significantly predict low levels of burnout, secondary traumatic stress, and psychological distress. In addition, high levels of burnout and secondary traumatic stress were hypothesised to significantly predict high levels of psychological distress.

2 Method

2.1 Participants

The sample consisted of 133 human service professionals working in the fields of psychology, social work, counseling, youth and foster care work. The age range of the participants was 20 to 64 years(M_age = 39.20, SD = 11.13) with 106 (79.7%) female participants and 27 (20.3%) male participants.

2.2 Materials

The psychometrically sound questionnaires participants completed to measure psychological distress, burnout, secondary traumatic stress, mindfulness, and resilience were as follows. The General Well-Being Schedule (GWBS) [32] an 18-item questionnaire with two subscales that measure psychological well-being and distress. The psychological distress subscale assesses emotional suffering in terms of anxiety (i.e., restlessness, feeling tense) and depressive (i.e., sadness, hopelessness) symptomology. The Professional Quality of Life Scale – Version 5 (ProQOL-5) [7] a 30-item scale with two subscales; the burnout subscale which assesses exhaustion, frustration, anger, and depression typical of burnout and the secondary traumatic stress subscale which assesses negative feelings driven by fear and work-related trauma. The Resilience Factor Inventory (RFI) [33], a 60-item scale measuring an individual’s current level of resilience. The Frieburg Mindfulness Inventory (FMI) [34] a 14-item scale measuring mindfulness.

3 Results

3.1 Correlational analysis

Intercorrelations, uncentred means and standard deviations are shown in Table 1. All variables were significantly related, with psychological distress, burnout, and secondary traumatic stress being significantly negatively related to resilience and mindfulness. Psychological distress, burnout, and secondary traumatic stress were significantly positively related. Likewise, resilience and mindfulness were significantly positively related.

3.2 Hierarchical analysis one: Burnout

Preliminary analysis revealed age was a significant predictor of resilience (r = 0.26, p = 0.003) and mindfulness (r = 0.26, p = 0.003). However age was not related to burnout (r = –0.09, p = 0.294). Gender, education, and employment status were not related to any of the criterion or predictor variables. As preliminary analysis found age covaried with the criterion and predictor variables, it was entered on Step 1 of the hierarchical multiple regression analysis. Resilience was entered on Step 2 and mindfulness on Step 3. After Step 3, when age, resilience, and mindfulness had been entered into the regression equation, a significant amount of variance in burnout was accounted for (R² = 0.45, adjusted R² = 0.44, F(3, 129) = 35.15, p < 0.001). The R² value of 0.45 indicates that the model with all predictors in it accounts for 45% of the variance in burnout.

At Step 1 age was not a significant contributor to the model, accounting for 0.8% of the variance in burnout, R²_change = 0.01, F_change = (1, 131) = 1.11, p = 0.294. At Step 2, resilience accounted for a significant 37.3% of the variance in burnout, R²_change = 0.37, F_change = (1, 130) = 78.43, p < 0.001. Higher scores on resilience were affiliated with lower levels on burnout. At Step 3, mindfulness accounted for an additional 6.8% of the variance in burnout, R²_change = 0.07, F_change = (1, 129) = 15.99, p < 0.001. Higher scores on mindfulness were also affiliated with lower scores on burnout. Examination of the part correlation coefficients revealed age contributed 0.98% unique variance to burnout, resilience contributed 8.88% unique variance and mindfulness contributed 6.81% unique variance. 28% of the variance in burnout was attributable to shared variability amongst the predictor variables.

3.3 Hierarchical analysis two: Secondary traumatic stress

Age was entered on Step 1, resilience was entered on Step 2, and mindfulness on Step 3. After Step 3,when age, resilience, and mindfulness had been entered into the regression equation, a significant amount of variance in secondary traumatic stress was accounted for (R² = 0.26, adjusted R² = 0.24, F(3, 129) = 14.69, p < 0.001). The R² value of 0.26 indicates that the model with all predictors in it accounts for 26% of the variance in secondary traumatic stress.

For secondary traumatic stress, age contributed 0.1% of the variance in the model at Step 1, however it was not a significant predictor, R²_change = 0.00, F_change = (1, 131) = 0.09, p = 0.771. At Step 2, resilience accounted for a significant 23.6% of the variance in secondary traumatic stress, R²_change = 0.24, F_change = (1, 130) = 40.28, p < 0.001. Higher scores on resilience were affiliated with lower levels on secondary traumatic stress. At Step 3, mindfulness accounted for a non-significant 1.8% of the variance in secondary traumatic stress, R²_change = 0.02, F_change = (1, 129) = 3.05, p = 0.083. Examination of the part correlation coefficients revealed age contributed 1.32% unique variance to secondary traumatic stress, resilience contributed 8.18% unique variance and mindfulness contributed 1.77% unique variance. 15% of the variance in secondary traumatic stress was attributable to shared variability amongst the predictor variables.

3.4 Hierarchical analysis three: Psychological distress one

Age was entered on Step 1, resilience was entered on Step 2, and mindfulness on Step 3. After Step 3, when age, resilience, and mindfulness had been entered into the regression equation, a significant amount of variance in psychological distress was accounted for (R² = 0.40, adjusted R² = 0.39, F(3, 129) = 29.05, p < 0.001). The R² value of 0.40 indicates that the model with all predictors in it accounts for 40% of the variance in psychological distress.

At Step 1, age contributed a significant 4.7% of the variance in the model, R²_change = 0.05, F_change = (1, 131) = 6.47, p = 0.012. At Step 2, resilience accounted for a significant 30.4% of the variance in psychological distress, R²_change = 0.30, F_change = (1, 130) = 61.04, p < 0.001. Higher scores on resilience were affiliated with lower levels on psychological distress. At Step 3, mindfulness accounted for an additional 5.2% of the variance in psychological distress, R²_change = 0.05, F_change = (1, 129) = 11.18, p = 0.001. Higher scores on mindfulness were also affiliated with lower scores on psychological distress. Examination of the part correlation coefficients revealed age contributed a 0.23% unique variance to psychological distress, resilience contributed 7.56% unique variance and mindfulness contributed 5.15% unique variance. 27% of the variance in psychological distress was attributable to shared variability amongst the predictor variables.

3.5 Hierarchical analysis four: Psychological distress two

Age was entered on Step 1, burnout was entered on Step 2, and secondary traumatic stress on Step 3. After Step 3, when age, burnout, and secondary traumatic stress had been entered, a significant amount of variance in psychological distress was accounted for (R² = 0.42, adjusted R² = 0.40, F(3, 129) = 30.88, p < 0.001). The R² value of 0.42 indicates that the model with all predictors in it accounts for 42% of the variance in psychological distress.

Age was a significant contributor to the model, accounting for 4.7% of the variance in the model at Step 1, R²_change = 0.05, F_change = (1, 131) = 6.47, p = 0.012. At Step 2, burnout accounted for a significant 36.5% of the variance in psychological distress, R²_change = 0.37, F_change = (1, 130) = 80.87, p < 0.001. Higher scores on burnout were affiliated with higher levels on psychological distress. At Step 3, secondary traumatic stress accounted for a non-significant 0.5% of the variance in psychological distress, R²_change = 0.01, F_change = (1, 129) = 1.21, p = 0.273. Examination of the part correlation coefficients revealed age contributed 2.70% unique variance to psychological distress, secondary traumatic stress contributed 0.55%, and burnout contributed 15.68% unique variance. 22% of the variance in psychological distress was attributable to shared variability amongst the predictor variables.

4 Discussion

The results found that higher levels of resilience and mindfulness significantly predicted lower levels of burnout supporting previous research [16, 28]. The results indicated that age was not a significant predictor of burnout consistent with previous research [31]. Resilience was found to be a significant predictor of low levels of secondary traumatic stress consistent with previous research [35]. However, mindfulness was not found to be a significant predictor, which was inconsistent with previous studies [9]. Contrary to expectations, age did not significantly predict low levels of secondary traumatic stress indicating that regardless of age, human service professionals can experience secondary traumatic stress.

Age, mindfulness, and resilience were significant predictors of psychological distress. Previous research suggests that resilient employees have the ability to monitor and regulate their emotions, maintain focus when managing stressful events [33]. Secondary traumatic stress was not found to be a predictor of psychological distress, however, in support of previous findings, burnout was shown to predict psychological distress [31].

Whilst age was not a significant predictor of burnout or secondary traumatic stress, it did add significant variance to psychological distress. That is, the older the human service professional the less psychological distress they experience. Post hoc interpretation suggests that acquiring the skills and expertise to reduce the risk of experiencing psychological distress may develop with age [36].

5 Limitations

The current study has limitations, which warrant consideration. Participation in the study was not a requirement for employees, as they were invited to participate on a voluntary basis. It is unknown if this resulted in a self-selection bias or if those employees who were perhaps experiencing the greatest levels of psychological distress were too over-extended to participate. The reliance of self-report data may also be a limitation, as self-report data can result in social desirability bias and demand characteristics. In addition, interpretation is limited as the current study employed a cross-sectional correlational design, and as a result, limits the extent to which causal inferences can be made. For example, whilst resilience was found to explain unique variance in psychological distress, it cannot be established whether human service professionals who report low levels of psychological distress do so as a result of their higher level of resilience. However, the results do indicate that there are significant relationships between resilience, mindfulness, burnout, secondary traumatic stress, and psychological distress and future research could establish causal priority by experimentally manipulating variables of interest.

Furthermore, experimental manipulation of the variables will allow a cause and effect relationship to be determined, such as evaluation or workplace interventions targeted at reducing levels of psychological distress, burnout, and secondary traumatic stress, and increasing resilience and mindfulnessskills.

6 Conclusion

Findings from this research contribute to the understanding of factors that have the potential to reduce the risk of negative psychological outcomes among human service professionals. The results of the current study provide support for the argument to develop programs that focus on cultivating resilience and mindfulness among human service professionals to help reduce the risk of burnout, secondary traumatic stress, and psychological distress.

Conflict of interest

The authors have no conflict of interest to report.

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