Dealing with Workload and Stress Complaints: The Impact of Resilience ()
1. Introduction
Mental health care professionals (MHP) are confronted with an increasingly demanding environment. MHP experience high pressure due to a combination of several factors, including a shortage of staff, increasing waiting lists, restrictive regulations and increase in administrative burdens, budgetary constraints, and high expectations from society. Van den Broek et al. (2022) stated that future capacity problems and above mentioned factors justify to raise the alarm for MHP and ensure support and interventions stimulating health promotion in the workplace. In an earlier study, Van den Broek and De Vroege (2023b) compared situations of MHP during- and post-pandemic and found that the number of mental health complaints decreased significantly post-pandemic. Furthermore, most MHP had restored their work-life balance after the pandemic as well. This resilience of MHP was called remarkably. Investigating the relationship between perceived workload, mental well-being, and resilience among MHP therefore seems to be pivotal in order to gain more insight into the sustainable employability of MHP.
During the past decade, the fourth goal of the Quadruple Aim model: ‘improving well-being of the healthcare team’ (Bodenheimer & Sinsky, 2014) is becoming more important in relation to the other three goals: improving population health, enhancing patient experience, and reducing costs. In short, without well-functioning MHP, high-quality care cannot be provided! In order to function sustainably themselves, mental health institutions are in need of MHP who are able to work independently and healthily for a long time. One can consider this as a parallel process, which leads to shifting the focus of organizations’ human resource management (HRM) policies from strategic to sustainable (Kramar, 2014).
Sustainable employability is about the ability of workers to function both at work and in the labor market throughout their working lives (Van der Klink et al., 2016). A recent scoping review found that sustainable employability among healthcare workers is under strain due to high workload, a shortage of employees, and absenteeism based on mental complaints (Van den Broek et al., 2023c). In the same study it was stated that the COVID-19 pandemic had a huge impact on the sustainability of healthcare workers, leading to increased mental health problems, absenteeism, and changes in the perspective on work because of the disrupted work-life balance. A follow-up comparative study by Nagel and Nilsson (2022) on the work-related mental health of nurses before and during the COVID-19 pandemic found that an increased workload and lack of peer support had an increased association with work-related mental health conditions.
Several studies have found that workload has a major impact on the mental health of healthcare workers and the sustainability of healthcare systems (Fitzgerald et al., 2017; Doleman et al., 2023). The lack of sustainable healthcare education about the necessary attention to one’s own mental health during the training of mental healthcare workers further exacerbates these challenges (Bray et al., 2023). To promote sustainable practices within healthcare workflows, it is critical to prioritize supportive conditions (Frenkel et al., 2022), adopt various educational approaches (Bray et al., 2023), develop better organizational practices, and implement sector-specific crisis measures (Frenkel et al., 2022) to effectively address work stressors.
Resilience is a complicated concept that several researchers have published on. Therefore, resilience is difficult to measure because it evaluates over time, according to Van Harmelen (2022). Van Harmelen (2022) and Ioannidis et al., (2020) described resilience as “a complex dynamic process of positive adaptation to stress that occurs and unfolds over time, and differs between persons and over time. The process of positive adaptation exists of complex interactions of neurobiological and social qualities, stress, and the context in which these interactions take place”. Jackson et al., (2007) described resilience, as “a dynamic process encompassing adaptation within the context of significant adversity”. Furthermore, resilience is often considered a counterpart to poor well-being, which creates workplace stressors, setbacks, and seen as a buffer against difficult circumstances (McAllister & McKinnon, 2009). Jensen et al., (2008) identified four aspects of physician resilience, relating to attitudes and perspectives (e.g., valuing role), balance and prioritization (e.g. scheduling time off) practice management (e.g. efficient organization) and supportive relationships. Jensen et al., (2008) identified four components that determine resilience among HealthCare workers: attitudes and perspectives; balance and prioritization; management of day-to-day practice and finally supporting relationships. In addition, Eley et al., (2013) related several personal traits to resilience, including the degree of independence, responsibility, optimism, persistence, and cooperativeness. Based on this vision of resilience it is clear that resilience is a concept that can change during a person’s life under the influence of factors discussed above. In this current study we adapt the aforementioned description of resilience as ‘a complex dynamic process of positive adaptation to stress’ with the addition of Eley et al., (2013) with regard to its’ adaptability or change over time.
The aim of this study is to investigate the associations between workload, mental well-being, and resilience in MHP and to investigate whether resilience plays a mediating role in the relationship between perceived workload and mental well-being. The current study used a large convenience sample of Dutch behavioral therapist in order to investigate these associations. In addition, this current study explored factors that impacted the increase of workload and explored what may contribute to reducing perceived workload in mental health care, according to MHP.
2. Materials and Methods
2.1. Design and Hypotheses
This cross-sectional study focused on the relation between workload, resilience and mental well-being among MHP (more specific behavioral therapists). In this article, the general term MHP will be used for the respondents who are specifically also behavioral therapists. We hypothesized that, as shown in previous research, there is a negative relationship between workload and mental well-being. Our main hypothesis states that resilience, described as ‘a complex dynamic process of positive adaptation to stress’, mediates the relationship between workload and well-being. In addition, it is explored to what extent certain work-related aspects cause workload according to the MHP, and on top of that, we are curious about what could be changed in their opinion to reduce this workload.
2.2. Procedure
The current study utilized a convenience sample existing of a cross-sectional dataset of 716 MHP that are members of the Association for Behavioral and Cognitive therapies (in Dutch: Vereniging voor Gedragsen Cognitieve therapieën, VCGt) in the Netherlands. Members of the VGCt were invited to participate in the study by conducting an online survey during the autumn VGCt conference in 2023. Participants were able to access the online survey via a link on the VGCt website. The survey commenced with a short introduction that included the study purpose, the implications of participation, and the procedure of data processing and privacy handling. Following this, participants were requested to confirm their consent to participate in the study. The survey started with socio-demographic questions (i.e., age, gender and work setting), questions about their perceived workload (i.e., work pressure, stressful tasks and stressful amount of work, see section 2.4.2.) and mental well-being (i.e., stress, anxiety, depression, cognitive complains and energy balance, see section 2.4.3.). Next participants were asked to indicate what characteristics of their job give them the most workload and what according to them could contribute to reducing the workload in mental health care (see section 2.4.5). Finally, resilience was measured by asking participants to indicate to which extend a list of characteristics apply to them (see section 2.4.4). The data was collected from 21-Nov-2023 to 30-Dec-2023. Inclusion criteria for this study required direct provision of patient care. All data were stored anonymously, with only researchers having access to the data
2.3. Participants
A total of 716 VGCt-members/MHP participated in the online survey. One of them had missing data on all questionnaires and four of the respondents did not meet the inclusion criteria of providing direct care: two respondents worked in education, one worked at a health insurance company, and one was retired. Ultimately, the data of 711 respondents who met the inclusion criteria were used. In total, 485 (68.2%) work in an outpatient setting, 105 (14.7%) in a part-time or clinical setting, and 121 (17.0%) stated to have their own practice.
2.4. Measurements
2.4.1. Socio-Demographics
Socio-demographic variables age, gender, and work setting (i.e., private practice yes or no) were obtained.
2.4.2. Workload
Three variables of workload were measured by asking the following questions: 1) “How much work pressure do you experience at your current job?” (Work pressure), 2) “How stressful do the tasks you perform at work feel?” (stressful tasks), 3) “How stressful does the amount of work you have to do feel?” (stressful amount of work). Participants rated the questions using a ten point Likert scale ranging from 0 (not) to 10 (very). Workload was calculated as the mean of the mean score on the three variables.
2.4.3. Mental Well-Being
Five variables of mental well-being were measured by asking the following questions: a) “How many stress complaints do you experience because of your work?” (Stress complaints), b) “How many anxiety complaints do you experience because of your work?” (Anxiety complaints), c) “How many complaints of depression do you experience because of your work?” (Depression complaints), d) “How many cognitive complaints do you experience as a result of your work?” (Cognitive complaints) and e) “Is your energy level still in balance after a day of work?” (Energy-balance). Participants scored the questions using a ten-point Likert-scale, ranging from 0 (not) to 10 (very). Mental well-being was calculated as the mean of the mean scores on the five variables.
2.4.4. Resilience
At the time of the measurement, there were not many questionnaires available to measure the concept of resilience. In the study, the questionnaire “How Resilient are you?”, was used, developed by Professor Al Siebert, professor of psychology at Portland State University (USA) and founder of Al Siebert Resiliency Center in Portland USA (Siebert, 2010). The variable resilience comprised ten items measured by asking the following questions: a) “I adapt quickly. I recover quickly after difficulties”, b) “I set priorities and act accordingly; I say no to things I don’t have time for”, c) “I am optimistic, I see problems as temporary. I expect to recover and everything will be fine.”, d) “During a crisis, I calm myself down and focus on taking useful actions”, e) “I am good at logically analyzing problems”, f) “I come up with creative solutions. I trust my intuition.”, g) “I am playful, have a sense of humor, can laugh at myself.”, h) “I am curious and ask questions. I like to know how things work and I like to experiment.”, i) “I am constantly learning from my experiences and those of others.”, j) “I am flexible. I can deal with conflicting feelings, such as trust and distrust.” Participants rated the questions using a ten point Likert scale ranging from 0 (not) to 10 (very). Resilience was calculated as the mean of the mean scores on all items.
2.4.5. Work-Related Aspects that Increase and Reduce Workload
The extent in which certain work-related aspects are perceived as increasing workload and reducing workload by MHP were explored by asking respondents the following two questions: a) “What parts of your job causes the most workload?” and b) “What do you think can contribute to reducing the workload in mental health care?” Twenty work-related aspects were presented as option for the first question and eleven work-related aspects were presented as option for the second question. Participants were asked to indicate which of the mentioned aspects applies to them.
2.4.6. Data Analysis
Data analyses were performed using Statistical Package for the Social Sciences Version 29. First, data was checked on outliers, missing variables, and normality. Independent sample t-tests and analysis of variance (ANOVA) were used to test group differences on mental well-being, workload and resilience. Pearson correlations were calculated for demographic characteristics and workload, mental well-being and resilience. Next, the impact of workload on mental well-being through the possible mediating factor of resilience was tested (see Figure 1 for a visual representation). We used the classical approach to mediation (see Baron & Kenny, 1986) according to which there is a mediation effect when the effect of: workload onto resilience is significant (path a); resilience onto mental well-being is significant (path b); workload onto mental well-being is significant (path c); and the direct effect of workload on mental well-being (path c’) is closer to zero than the indirect effect (path c). Therefore, three (separate) regression analyses were used to test the mediation effect: a regression from workload onto resilience (path a), a multiple linear regression from workload and resilience onto mental well-being (path b and c’); and a regression from workload onto mental well-being (path c). Finally, Pearson correlations were calculated to investigate which work-related aspects are perceived by participants as increasing and reducing workload. Significance level was set at .05.
3. Results
3.1. Descriptive Statistics
Table 1 presents the characteristics of respondents and descriptive statistics of the study variables, including mean and standard deviation. The age of the respondents varied from 25 to 75 years old, with a mean of 43.19 years (SD = 11.44). Most respondents were female (89.3%, n = 635) and had no private practice and were employed in an outpatient, part-time or clinical setting (83.0%, n = 590).
Table 1. Sample characteristics.
| Variable |
TotalM (SD)/n (%) |
mental well-being |
workload |
resilience |
| M (SD) |
t/F |
M (SD) |
t/F |
M (SD) |
t/F |
| Age |
43.19 (11.44)a |
|
1.05 |
|
2.32** |
|
.91 |
| Gender |
|
.19 |
1.41 |
−2.79** |
| Female |
635 (89.8)b |
3.91 (1.49) |
6.98 (1.63) |
3.78 (0.47) |
| Male |
72 (10.2)b |
3.87 (1.47) |
6.68 (2.05) |
3.95 (0.47) |
| Private practice1 |
|
1.07 |
36.08*** |
.85 |
| yes |
121 (17.0)b |
3.61 (1.51) |
6.00 (2.20) |
3.78 (0.53) |
| no |
590 (83.0)b |
3.96 (1.48) |
7.14 (1.48) |
3.78 (0.46) |
| Workload |
6.95 (1.68)a |
|
|
|
|
|
|
| Work pressure |
7.16 (1.77)a |
|
|
|
|
|
|
| Stressful tasks |
6.48 (1.79)a |
|
|
|
|
|
|
| Stressful amount of work |
7.21 (1.95)a |
|
|
|
|
|
|
| Mental well-being |
3.90 (1.49)a |
|
|
|
|
|
|
| Stress complaints |
5.87 (2.14)a |
|
|
|
|
|
|
| Anxiety complaints |
3.10 (2.52)a |
|
|
|
|
|
|
| Depression complaints |
2.59 (2.36)a |
|
|
|
|
|
|
| Cognitive complaints |
3.44 (2.63)a |
|
|
|
|
|
|
| Energy balance |
4.52 (2.27)a |
|
|
|
|
|
|
| Resilience |
3.80 (0.48)a |
|
|
|
|
|
|
| Adapt to changes |
3.85 (0.72)a |
|
|
|
|
|
|
| Set priorities and boundaries |
3.20 (0.93)a |
|
|
|
|
|
|
| Optimistic and overcomes problems |
3.81 (0.85)a |
|
|
|
|
|
|
| Focused during crisis |
3.82 (0.79)a |
|
|
|
|
|
|
| Logically analysing problems |
4.07 (0.79)a |
|
|
|
|
|
|
| Come up with creative solutions |
3.80 (0.83)a |
|
|
|
|
|
|
| Playful and humorous |
3.84 (0.91)a |
|
|
|
|
|
|
| Curious and likes to experiment |
3.97 (0.81)a |
|
|
|
|
|
|
| Learning form experiences |
4.12 (0.74)a |
|
|
|
|
|
|
| Able to deal with conflicting feelings |
3.49 (0.80)a |
|
|
|
|
|
|
** = p < .01; *** p < .001 (2-tailed); a values represent mean (SD), b values represent n (%).
A mean score of 6.95 (SD = 1.68) was found for workload, consisting of the three variables that measured the perceived level of: work pressure (M = 7.16, SD = 1.77), stressful tasks (M = 6.48, SD = 1.79) and stressful amount of work (M = 7.21, SD = 1.95). Further, a mean score of 3.90 (SD = 1.49) was found for mental well-being, consisting of the variables measuring stress (M = 5.87, SD = 2.14), anxiety (M = 3.10, SD = 2.52), depression (M = 2.59, SD = 2.36), cognitive complaints (M=3.44, SD = 2.63) and energy balance (M =4.52, SD = 2.27). Finally, a mean score of 3.80 (SD = 0.48) was found for resilience, consisting of ten items measuring the ability to adapt to changes (M = 3.85, SD = 1.72) and set priorities and boundaries (M = 3.20, SD = 0.93), and to be optimistic and overcomes problems (M = 3.81, SD = 0.85), to be focused during a crisis (M = 3.82, SD = 0.79), logically analyzing problems (M = 4.07, SD = .79), to come up with creative solutions (M = 3.80, SD = 0.83), to be playful and humorous (M = 3.84, SD = .91), to be curious and like to experiment (M = 3.97, SD = .81), to learn from experiences (M = 4.12, SD = .74) and able to deal with conflicting feelings (M = 3.49, SD = .80).
Significant differences were found with respect to age in workload (F = 2.32, p < .001), with participants with higher age scoring lower on workload (i.e. work pressure and stressful amount of work, see also Table 2). Further, significant gender differences were found on resilience (t = −2.79, p < .05), with men scoring higher compared to woman. Also, significant differences were found with respect to private practice on workload (F = 36.08, p < .001), with participants who (also) work in a private practice scoring lower on workload compared to those who are employed in an outpatient, part-time or clinical setting.
3.2. Association between Work Pressure, Mental Well-Being, Resilience and Demographic Variables
Bivariate correlations between workload, mental well-being and resilience are shown in Table 2. Results show that workload and mental well-being were positively associated (r = 0.50, p < .01), indicating that higher scores on workload are associated with higher scores of mental complaints. All three variables of workload were significantly positively correlated with stress, anxiety, depression and cognitive complaints and negatively with energy balance (see Table 2, all p < .01), indicating that higher levels of experienced work pressure, stressful tasks and stressful amount of work are associated with higher scores of stress, anxiety, depression and cognitive complaints and lower levels of energy balance. Further, workload and resilience were negatively related (r = −0.10, p < .05), indicating that higher levels of workload are associated with lower levels of resilience. Looking at the items of workload, only the item stressful-tasks was significantly related to resilience (r = −0.16, p < .01), indicating that higher levels stressful-tasks are associated with lower levels of resilience. Finally, resilience and mental well-being were negatively associated (r = −0.29, p < .01), indicating that higher levels of mental complaints were reported by MHP reporting lower levels of resilience. Resilience was negatively correlated with stress, anxiety, depression and cognitive complaints and positively with energy balance (see Table 2, all p < .01), indicating that higher levels of stress, anxiety, depression and cognitive complaints and lower levels of energy balance were reported by MHP reporting lower levels of resilience. Further, older age (r = −0.12, p < .01) and working in a private practice (r = −0.09, p < .05) were associated with less mental complaints. Next, older age (r = −0.12, p < .01) and working in a private practice (r = −0.21, p < .01) were associated with lower workload. Finally, male gender (r = 0.12, p < .01) and working in a private practice (r = 0.09, p < .05) were associated with more resilience.
Table 2. Correlations among study variables.
| Total scores of workload, mental well-being and resilience |
|
1 |
2 |
3 |
4 |
5 |
6 |
| Age |
- |
|
|
|
|
|
| Gender |
−.14** |
- |
|
|
|
|
| Private practice1 |
.40** |
−.12** |
- |
|
|
|
| Workload |
−.12** |
.03 |
−.21** |
- |
|
|
| Mental well-being |
−.12** |
.01 |
−.09* |
.50** |
- |
|
| Resilience |
.04 |
−.12** |
.09* |
−.10* |
−.29** |
- |
| Specified to the items of workload and mental well-being |
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
| Age |
- |
|
|
|
|
|
|
|
|
|
|
|
| Gender |
−.14** |
- |
|
|
|
|
|
|
|
|
|
|
| Private practice1 |
.40** |
−.12** |
- |
|
|
|
|
|
|
|
|
|
| Work pressure |
−.13** |
.10* |
−.25** |
- |
|
|
|
|
|
|
|
|
| Tasks |
−.06 |
−.03 |
−.15** |
.63** |
- |
|
|
|
|
|
|
|
| Amount of work |
−.16** |
.03 |
−.19** |
.77** |
.60** |
- |
|
|
|
|
|
|
| Stress |
−.14** |
.08* |
−.18** |
.62** |
.59** |
.57** |
- |
|
|
|
|
|
| Anxiety |
−.14** |
.05 |
−.04 |
.29** |
.41** |
.28** |
.56** |
- |
|
|
|
|
| Depression |
−.08* |
.02 |
−.03 |
.35** |
.50** |
.32** |
.61** |
.64** |
- |
|
|
|
| Cognitive complaints |
−.13** |
.04 |
−.12** |
.38** |
.42** |
.38** |
−.57** |
.52** |
.58** |
- |
|
|
| Energy-balance |
.13** |
−.13** |
.15** |
−.29** |
−.32** |
−.28** |
−.35** |
−.20** |
−.29** |
−.30** |
- |
|
| Resilience |
.04 |
−.12** |
.09* |
−.08 |
−.16** |
−.05 |
−.22** |
−.30** |
−.28** |
−.22** |
.14** |
- |
* = p < .05, ** = p < .01 (2-tailed); 1 the variable private practice concerns whether or not a participant (also) works in their own practice.
3.3. Resilience as a Mediator in the Association between Workload and Mental Well-Being
Next, it was tested whether resilience mediates the relationship between workload and mental well-being (see Table 3). Results show that the relationship between workload and resilience (path a, see Figure 1) was negatively and significant (B = −0.03, t = −2.70, p = .007). The relationship between resilience and mental well-being (path b, see Figure 1) was negative and significant (B = −0.76, t = -6.95, p < .001). Further, the total effect of workload on mental well-being (path c, see Figure 1) was significant (B = 0.45, t = 14.15, p < .001). The overall findings demonstrated a positive and significant impact on workload on mental well-being (path c’, see Figure 1) through resilience (B = 0.42, t = 13.46, p < .001). In sum, the results show that all the requirements for mediation are met: the effects a, b and c are all statistically significant and the direct effect of workload on mental well-being c’ = 0.42 and thus closer to zero than the total effect c = 0.45. As both c and c’ are significant, resilience partially mediates the effect from workload onto mental well-being (i.e., it decreases the effect but does not nullify it altogether).
Table 3. Mediation analysis.
| Direct effect model |
| Predictor |
|
Outcome = M (resilience) |
|
|
B |
SE |
t |
p |
| X (workload) |
a |
−0.03 |
0.12 |
−2.70 |
.007** |
| Age |
|
−0.00 |
0.00 |
−1.15 |
.249 |
| Gender |
|
−0.16 |
0.06 |
−2.57 |
.010* |
| Private practice1 |
|
0.06 |
0.06 |
1.06 |
.291 |
| Constant |
i1 |
4.24 |
0.14 |
30.51 |
<.001*** |
| Direct effect model |
| Predictor |
|
Outcome (mental well-being) |
|
|
B |
SE |
t |
p |
| X (workload) |
c’ |
0.42 |
0.03 |
13.46 |
<.001*** |
| M (resilience) |
b |
−0.76 |
0.11 |
−6.95 |
<.001*** |
| Age |
|
−0.01 |
0.01 |
−1.30 |
.194 |
| Gender |
|
−0.24 |
0.17 |
1.44 |
.151 |
| Private practice1 |
|
0.22 |
0.16 |
−1.44 |
.150 |
| Constant |
i2 |
4.30 |
0.60 |
7.27 |
<.001*** |
| Total effect model |
| Predictor |
|
Outcome = Y (mental well-being) |
|
|
B |
SE |
t |
p |
| X (workload) |
c |
0.45 |
0.32 |
14.15 |
<.001*** |
| Age |
|
−0.00 |
0.01 |
−0.88 |
.381 |
| Gender |
|
−0.10 |
0.17 |
−0.58 |
.565 |
| Private practice1 |
|
0.23 |
0.16 |
1.45 |
.149 |
| Constant |
i3 |
0.982 |
0.38 |
2.61 |
.009** |
* = p < .05, ** = p < .01; *** p < .001 (2-tailed); SE = standard error, 1 the variable private practice concerns whether or not a participant (also) works in their own practice.
Figure 1. Mediation model of resilience in the relationship between workload and mental well-being. ** = p < .01; *** = p < .001 (2-tailed).
3.4. Work-Related Aspects and Perception of MHP on Impact on Workload
Finally, it was explored to what extent certain work-related factors are perceived by MHP as causing increasing workload and, in their opinion, can contribute to reducing workload in mental health care (see Table 4). Results show that with regard to the question in what extend certain work-related aspects cause workload 59.3% of the respondents mentioned too little time for non-billable tasks, 58.1% increase in the severity of problems with clients, 56.0% administrative burden, 48.5% own character, 48.2% increasing production standards, 40.2% waiting lists, 36.8% staff/capacity shortage, 31.6% staff turnover, 26.5% cooperation with management, 26.3% large caseload as a coordinating practitioner and 25.4% organizational pressure, too little support from management, 16.7% long-term illness of colleague, 15.9% (further) training not properly facilitated, 12.4% supervision and/or intervision not properly facilitated, 11.9% having little autonomy, 10.0% composition of the team, 8.3% collaboration with colleagues, 5.5% crisis service, and 4.8% long-term leave of colleague. With regard to the question which aspects could reduce workload 48.8% of the respondents mentioned building relaxation into the workplace, 31.1% increasing social support in the workplace, 30.5% discussing mental complaints due to work pressure, 28.1% possibility of supervision and/or intervision, 26.4% informing clients about support from peers, 23.4% self-management of stress (monitoring one’s own energy balance), 20.4% possibility of (further) training, 18.1% setting up a support system within work (e.g. buddies and/or support department), 13.1% psycho-education mental self-care and prevention plan for employees, and 8% increase social support outside the work environment.
Table 4. Work-related aspects associated with workload.
| Variable |
n (%) |
| Aspects that are perceived as causing workload |
|
| Not enough time for non-billable tasks |
422 (59.3) |
| Increasing severity of clients’ problems |
414 (58.1) |
| Administrative burden |
399 (56.0) |
| Own character |
345 (48.5) |
| Increasing production standards |
343 (48.2) |
| Waiting lists |
286 (40.2) |
| Staff/capacity shortage |
262 (36.8) |
| Staff turnover |
225 (31.6) |
| Cooperation with management |
189 (26.5) |
| Large caseload as a coordinating practitioner |
187 (26.3) |
| Organizational pressure, too little support from management |
181 (25.4) |
| Long-term illness of colleague |
119 (16.7) |
| (Further) training not properly facilitated |
113 (15.9) |
| Supervision and/or intervision not properly facilitated |
88 (12.4) |
| Having little autonomy (e.g. no say in planning your agenda) |
85 (11.9) |
| Composition of the team |
71 (10.0) |
| Collaboration with colleagues |
59 (8.3) |
| Crisis service |
39 (5.5) |
| Long-term leave of colleague |
34 (4.8) |
| Aspects that could reducing workload according to participants |
|
| Building relaxation into the workplace |
347 (48.8) |
| Increasing social support in the workplace |
221 (31.1) |
| Discussing mental complaints due to work pressure (destigmatization) |
217 (30.5) |
| Possibility of supervision and/or intervision |
200 (28.1) |
| Informing clients about support form peers |
198 (26.4) |
| Self-management of stress (monitoring one’s own energy balance) |
167 (23.4) |
| Possibility of (further) training |
145 (20.4) |
| Setting up a support system within the work (e.g. buddies and a support department) |
129 (18.1) |
| Psycho-education mental self-care and prevention plan for employees |
93 (13.1) |
| Increase social support outside the work environment |
57 (8.0) |
4. Discussion
Mental health of MHP is becoming an increasingly important issue worldwide. Although well-being of MHP in general is considered as one of the cornerstones of professional effectiveness, health, and happiness (Eley et al., 2013), Within the past decades, less attention has been paid to this issue in clinical practice. Studies showed that mental well-being of MHP is undesirably low and that emotional exhaustion and drop-out among MHP in mental health care is high (Van den Broek & De Vroege, 2023a). These results may have negative consequences on sustainable employability among MHP which is under strain due to experienced high work pressure, an increasing capacity problem, and drop-out due to mental complaints (Van den Broek et al., 2023b). Howard et al., (2019) considered resilience as an antidote to a minimal sense of well-being, dealing with stressors, setbacks and negative experiences in the workplace and a buffer in dealing with complex situations. With this knowledge, it seems obvious to focus on (maintaining) mental resilience. Research amongst MHP into the relationship between perceived workload, mental well-being and resilience is scarce. This study mainly focused on the relationship between perceived workload, mental wellbeing, and resilience and especially whether the relation between perceived workload and mental well-being was mediated by resilience. In addition, we were interested in which factors contribute to the highest perceived workload and which issues can contribute to reducing the workload in mental health care, according to the MHP.
First, the results of the current study showed that workload and mental wellbeing are positively associated: meaning high experience workload is related to high(er) mental health issues. More specific, all three variables of workload (work pressure, stressful tasks and stressful amount of work) were significantly positively correlated with stress, anxiety, depression, and cognitive complaints and negatively with energy balance (i.e., less energy). This is in line with previous comparable research of Van den Broek et al., (2023c) which suggested that sustainable employability among healthcare workers is under strain due to high workload, and impactful events such as the COVID 19 pandemic that have led to increased mental health problems, absenteeism, and changes in the perspective on work because of the disrupted work-life balance.
Next, overall workload and resilience were negatively associated, which means that higher levels of workload were associated with lower levels of resilience. Only one item of workload, stressful-tasks, was significantly negatively associated to resilience, meaning that a higher level of stressful-tasks is associated with lower levels of resilience. Karadaş & Duran (2021) investigated the mediating role of resilience in the effect of perceived social support on work stress among healthcare workers. They also found a statistically negative correlation between work stress and perceived workload and resilience.
Furthermore, resilience and mental well-being were negatively associated which means that a higher levels of mental complaints were reported by MHP reporting lower levels of resilience. More specifically, resilience was negatively correlated with stress, anxiety, depression, and cognitive complaints and positively with energy balance, which means that higher levels of stress, anxiety, depression, and cognitive complaints and lower levels of energy balance were reported by MHP reporting lower levels of resilience. In several studies resilience is reported as a critical factor in maintaining energy balance and overall mental well-being of MHP. Resiliency is said to correlate inversely with burnout and psychological distress (anxiety, depression) (Castillo-González et al., 2023).
In addition, MHP with an older age and working in a private practice reported fewer mental complaints. The results showed that MHP of older age and working in a private practice are associated with a lower experienced workload. The explanation for this could be several; for instance, more experienced (maintaining interest, developing self-awareness, and accepting personal limitations), better role appreciation and more balance and prioritization (scheduling breaks for example), autonomy about practice management, build supportive relations in time (think of positive personal relationships, effective professional relationships, and good communication with colleagues) (Jensen et al., 2008), and several personality traits like maturity, ability to handle responsibilities (Eley et al., 2013).
Moreover, a gender difference was found: male MHP that are working in a private practice were associated with more resilience. Previous studies suggest that men and women in general respond differently to pressure and men report higher resilience scores compared to women. A cross-sectional study conducted by Lowe et al., (2021) in New York showed that female health professionals are more vulnerable to stress-related psychological consequences, such as symptoms of depression, anxiety, and post-traumatic stress disorder. This is in line with a study of Teo et al., (2024) among healthcare professionals in Singapore. They found that male gender was significantly associated with higher resilience scores. The same results were found in a cross-sectional study of Elkudssiah Ismail et al. (2022), in which male primary care physicians and community pharmacists in Malaisia reported higher resilience scores compared to their female counterparts. The possible explanation is that working women often also have domestic and family-related responsibilities, while men’s roles are mainly related to external work and decision-making (Hirani et al., 2016).
As hypothesized, it was found that the association between workload and mental well-being was mediated by resilience. This suggests that MHP reporting high scores on workload are more likely to experience higher levels of mental health complaints. Interestingly, however, resilience partly explains this relationship. MPH who experiences more workload report a lower score on resilience, which leads to an increase in reported mental health issues. This is in line with the starting points of the systematic review by Howard et al. (2019), in which they indicate that personal resilience is essential for mental health, recruitment and sustainability, to be able to deliver passionate client care.
Finally, the results based on the exploratory questions about which work-related aspects cause workload showed that nearly 60% of the respondents indicate that they have far too little time for non-billable tasks such as letters and telephone conversations with referrers and administrative tasks (e.g. resignation letters and reports, consultation with colleagues). More than half of the respondents indicated that the increasing severity of clients’ problems and the administrative burden as causing workload. Further, approximately half of the MHP perceive that workload is caused by increasing production standards, waiting lists, staff/capacity shortage and turnover. About a quarter of the MHP perceived that the cooperation with management, their large caseload as a coordinating practitioner, organizational pressure and too little support from their management increase their workload. Furthermore, MHP mentioned long-term illness of colleagues, inadequate resources like training not properly facilitated, supervision and/or intervision, having little autonomy, composition of the team, collaboration with colleagues, crisis service and long-term leave of colleague, as factors that increase workload. These findings are in line with the study of Howard et al. (2019), who found as causes of stress excessive workload, job and training demands, inadequate resources, poor supervision, working long hours (because of capacity problems; inadequate facilities and absence of support form colleague or management). These aspects lead to dissatisfaction and regret of career choice, job turnover (Yanchus et al., 2017) and lack of empathy for clients (Kealy et al., 2016).
With regards to aspects that could reduce workload, approximately half of the respondents mentioned building relaxation into the workplace. One third proposed increasing social support in the workplace, discussing mental complaints due to work pressure, and integrate the possibility of supervision and/or intervision. A quarter of them suggested informing clients about the possibility for support from peers, improving their own self-management of stress (monitoring one’s own energy balance) and creating possibilities of (further) training. Last recommendations concerned setting up a support system within work (e.g. buddies and/or support department), realize repeating psycho-education about mental self-care and prevention for employees, and increase social support outside the work environment. These findings are in line with previous review by De Sio, Buomprisco and Perri et al. (2020) on the effectiveness of preventive measures for work-related stress and preventive measures for mental complaints among healthcare professionals. The authors found various measures to be effective, such as collective interventions (e.g., support groups), individually targeted interventions (e.g., mentoring and physical activity) and mindfulness-based strategies (e.g., counselling programs). However, they also concluded that there is still no consensus on what the most effective preventive measures are. A review done by Howard et al., (2019) also indicated a thorough resilience intervention implemented at the workspace level. A supportive and collaborative working environment with stress-busting workshops should realize higher levels of well-being. Future research is needed to establish what interventions are effective for the several groups of MHP.
Strengths and Limitations
This current study focusses a scarce research topic in mental health science. A large sample was collected among behavioral therapists working all over the Netherlands in different occupational settings, which gives a good representation of the field of the MHP.
This study has several limitations. First, the lack of a clear operational definition of resilience is a limitation. However, based on the current available literature we tried to gauge on this topic in the best possible way by using the most up-to-date literature regarding this topic. the cross-sectional design only allowed for the factors, workload, well-being and resilience to be measured at a single time point, as a result we were unable to capture the dynamic changes of especially resilience over time. Hence, no conclusions regarding causality can be made. This is in contrast with the definition of resilience of Van Harmelen (2022) and Ioannidis et al., (2020) in which resilience is described as “a complex dynamic process of positive adaptation to stress that occurs and unfolds over time, and differs between persons and over time”. We choose for the combination of composite questionnaires, using a Likert scale to measure respondents’ perceptions of workload, mental well-being and resilience. However, these exploratory questionnaires lacked validation in combination. Further studies should be conducted using validated tools for assessing the relationship between workload, well-being, and psychological resilience. Moreover, since resilience is a complex construct, for which scientists have chosen different definitions, we decided to use the questionnaire of Al Siebert, (2010). It is possible that this questionnaire is not all-encompassing and that not all possible factors have been included in this study. Other confounding factors, such as mental and medical comorbidities, could have affected the resilience scores. Last, although the study may be generalized to other MHP, the external validity requires further exploration as the professional functions may differ from ours and above that, the study’s reliance on a convenience sample from one professional association may not accurately represent the broader population of MHP, which probably limits the generalizability of the findings.
5. Conclusion and Clinical Relevance
This study contributes to the understanding of the relationship between experienced workload, mental well-being, and resilience and the mediating role of resilience in relation to the perceived workload and mental well-being among MHP in the Netherlands. The findings emphasize the need to proactively address the issue of low resilience among MPH in Mental Health Care.
As a follow-up to this research, interventions aimed at enhancing resilience amongst MPH could be explored. These interventions can focus on the work area; building relaxation and increasing social support in the workplace; discussing mental complaints due to work pressure; creating a possibility of supervision and/or intervision and informing clients about support from peers, which can reduce the pressure on the professional. Interventions with respect to personal developments consists of regular self-awareness and reflection; self-care and skills training. With regard to non-work-related matters, more frequent discussions about taking days off on time should be advocated; achieving a good work-life balance and receiving support from partner or family. In addition, autonomy with regard to flexible working arrangements and scheduling of one’s own agenda, a culture of continuous learning in the workplace are mentioned as promoting resilience. In order to better evaluate the efficacy of potential interventions further research in this area is necessary. Although resilience is a complex concept, it appears that resilience plays a mediating role in dealing with an experienced workload and any mental health problems that can arise from it. Fostering resilience by implementing necessary strategies is highly desirable to maintain a healthy, effective, and enjoyable mental health work environment.
Acknowledgements
We would like to express our appreciation to Coen Horstman and Floor van Dun who took the initiative to conduct a survey among the members of the Dutch Association for Behavioral Therapy and gave us the opportunity to process the data. Lastly, we would like to thank all respondents who took the time to complete the survey.