Diagnosis of Psychosocial Risk Determinants and the Prioritization of Organizational Intervention Objects among Medical Occupational Groups in a Public Healthcare Institution ^†

Abstract

Background and Objectives: As the work environment is one of the most significant sources of stress, employers in the European Union are obliged to identify psychosocial risk determinants and take preventive measures to improve workers’ health and well-being while at work. The aim of this study was to determine which medical occupational group is the most exposed to stress and where any differences lie among medical occupational groups regarding the perception of psychosocial risk determinants and organizational intervention objects in the Lithuanian public healthcare institution. Materials and Methods: Using a cross-sectional study design, paper questionnaires were delivered to all health workers (n = 690) of the Lithuanian public healthcare institution; the response rate was 68% (n = 467). The questionnaire consisting of three parts was completed for the survey. It covered 14 psychosocial risk determinants and work-related stress, 10 organizational intervention objects, and sociodemographic data of health workers. Results: The results showed that perceived stress had mean rank scores differing statistically significant (p-value < 0.05) across occupational groups. The highest stress rating was given by the doctors’ group. Regarding psychosocial risk determinants, there were statistically significant differences (p-value < 0.05) in work overload scores among doctors, heads of units, and other health workers; in overtime scores and in tight deadlines scores between doctors and other health workers; in unclear role scores among all medical occupational groups; in being under-skilled for job scores between nurses and doctors; in responsibility for decision making scores among heads of units, doctors, and other health workers. Concerning organizational intervention objects, there were statistically significant differences (p-value < 0.05) in work-life balance scores, ensuring skills/abilities matching to the job demands scores, social support scores, organizational support scores, participation in decision-making scores, justice of reward scores, manager feedback scores, variety of tasks scores among heads of units, doctors, nurses, other health workers. Conclusions: The results of the study confirmed that different occupational groups emphasized different psychosocial risk determinants and organizational intervention objects. The findings suggest that focusing on the average worker does not have practical value and that it is important to understand the differential effects of different job characteristics on work outcomes considering occupational status while developing coping strategies in the institution. The risk group with the most exposure to stress were doctors in the healthcare institution.

1. Introduction

European Commission Directorate-General for Employment, Social Affairs, and Inclusion commissioned a survey designed to explore a range of questions about working conditions and occupational health and safety [1]. The survey revealed that amongst current workers, exposure to stress is considered as one of the main health and safety risks they face in their workplace (53%). The third edition of EU-OSHA’s ESENER survey was carried out in 2019, focusing particularly on the management of psychosocial risks such as work-related stress and harassment [2]. ESENER-3 showed that some of the psychosocial risk factors are present in a significant share of establishments in the EU28, namely having to deal with difficult patients, customers, and pupils (61%) and time pressure (44%). A growing body of research demonstrates that work-related stress can affect workers’ health and well-being. Work-related stress is associated with cardiovascular disease, diabetes, mental health and sleep disorders, and other health problems [3,4,5,6,7,8,9,10]. Stress at work harms not only employees’ health but also has negative consequences for the organization’s performance and national economy [11,12]. Despite a common understanding of psychosocial risks and ample evidence of the negative impact of these risks on workers’ health and organizational performance, the biggest problem remains psychosocial risk management and the practical application of empirical research findings [13,14]. One of the reasons may be that different occupations are affected by different stressors, and their stress level is determined by the interaction of many factors, such as job characteristics, organizational culture, regulatory mechanisms in the field of profession, etc. The research findings suggest that systematic assessment of risk groups on the basis of sociodemographic factors, especially occupational status, could facilitate psychosocial risk management in an organization [15,16]. According to Dudutienė, Juodaitė Račkauskienė, and Stukas’ research findings, occupational groups are the key factor that should be considered when managing psychosocial risks at the public primary healthcare institution [17]. Healthcare institutions are specific organizations and likely to comprise competing and overlapping occupational groups. “Thus, a key challenge to culture change programmes is to consider carefully the impact of change on specific groups (e.g., doctors, nurses and other health professionals, and managers) and to design appropriate policies to accommodate this” [18]. The study was designed to find out which medical occupational group is most exposed to stress and whether the perception of psychosocial risk determinants and the priorities of organizational intervention objects differ among medical staff holding different positions in the Lithuanian public healthcare institution.

2. Materials and Methods

The study, authorized by the administration, was conducted in one of the largest public primary healthcare institutions in Lithuania from February to March 2017. All 690 health workers employed in the institution were invited to participate on a voluntary basis. In order to guarantee anonymity and confidentiality in accordance with Lithuanian law, each health worker received information about the research and the paper questionnaire. The self-administrated questionnaire (instrument) has been introduced and used [17,19] in conducting complex stress management research in Lithuanian organizations. In this cross-sectional study, adapted to the health work version of the validated instrument was used [17].

Data were analyzed using the statistical software package IBM SPSS Statistics (Vilnius University, Vilnius, Lithuania). A descriptive analysis was carried out to examine the sociodemographic characteristics of health workers in the institution. The Kruskal–Wallis test for comparisons of the occupational groups were used then. Subsequently, pairwise comparisons were performed using Dunn’s procedure with a Bonferroni correction for multiple comparisons. Statistical significance was considered with p-value < 0.05 and 95% confidence interval (CI).

3. Results

A total of 467 health workers completed the survey. The response rate was 68%.

The descriptive analysis results [17] showed a predominance of women (94.9%), almost half of health workers (47.9%) were over 50 years of age, 350 of the health workers (76.1%) worked over 10 years, more than half of all health workers (52.9%) had university degrees, 38.5% of health workers had higher school degrees, and 8.6% of health workers had other levels of education. Regarding occupational status, the majority of health workers were nurses (43.9%), followed by doctors (28.3%), other health workers (21.6%), and heads of units (6.2%).

Tables and figures below present the attitudes of the occupational groups to the psychosocial risk determinants and organizational intervention objects (mean ranks, sample sizes (N), χ² values, with k-1 degrees of freedom and significance levels (p)).

3.1. Stress and Occupational Groups

The mean ranks of work-related stress scores were statistically significantly different between groups, χ²(3) = 12.14, p < 0.01 (

Table 1. Stress and occupational groups, results of the Kruskal–Wallis test.

Groups	N	Mean Rank	χ2(3)	p
Heads of units	29	183.29	12.14	<0.01
Doctors	132	262.90
Nurses	205	226.47
Other health workers	101	226.07

).

Subsequently, pairwise comparisons were performed using Dunn’s procedure with a Bonferroni correction for multiple comparisons. This post hoc analysis revealed statistically significant differences in work-related stress scores between doctors (262.90) and heads of units (183.29) (p = 0.016) (Figure 1).

3.2. Psychosocial Risk Determinants and Occupational Group

Results of the Kruskal–Wallis test [17] showed that six psychosocial risk determinants (work overload, χ²(3) = 13.41, p < 0.01; overtime χ²(3) = 14.23, p < 0.01; tight deadlines χ²(3) = 8.64, p = 0.03; unclear role, χ²(3) = 15.24, p < 0.01; being under-skilled χ²(3) = 10.30, p = 0.02; responsibility χ²(3) = 13.66, p < 0.01) had mean rank scores differing statistically across occupational groups.

The post hoc analysis revealed statistically significant differences in:

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Work overload scores between doctors (263.63) and heads of the units (187.41) (p = 0.028) and doctors and other health workers (211.15) (p = 0.015) (Figure 2).

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Overtime scores between doctors (263.42) and other health workers (200.73) (p = 0.001) (Figure 3).

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Tight deadlines scores between doctors (257.47) and other health workers (209.89) (p = 0.033) (Figure 4).

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Unclear role scores between heads of the units (152.50) and doctors (226.68) (p = 0.032), heads of the units and nurses (239.14) (p = 0.005), and heads of the units and other health workers (256.53) (p = 0.001) (Figure 5).

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Being under-skilled scores between doctors (212.52) and nurses (251.81) (p = 0.041) (Figure 6).

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Responsibility scores between other health workers (203.07) and doctors (252.87) (p = 0.016) and other health workers and heads of the units (282.62) (p = 0.016) (Figure 7).

3.3. Organizational Intervention Objects and Occupational Group

Results of the Kruskal–Wallis test [17] showed that all organizational intervention objects (except stress management training) had mean rank scores differing statistically across occupational groups: work–life balance, χ²(3) = 13.19, p < 0.01; skills/abilities matching to the job demands, χ²(3) = 15.29, p < 0.01; variety of tasks, χ²(3) = 51.06, p < 0.01; social support, χ²(3) = 9.33, p = 0.02; organizational support, χ²(3) = 17.88, p < 0.01; participation in decision making, χ²(3) = 8.08, p = 0.04; communication, χ²(3) = 10.10, p = 0.02; justice of reward, χ²(3) = 14.70, p < 0.01; manager feedback, χ²(3) = 15.65, p < 0.01.

The post hoc analysis revealed statistically significant differences in:

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Work–life balance scores between doctors (202.67) and heads of the units (282.10) (p = 0.017), and doctors and nurses (244.51) (p = 0.023) (Figure 8).

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Skills/abilities matching to the job demands scores between heads of the units (295.91) and other health workers (198.30) (p = 0.002) (Figure 9).

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Variety of tasks scores between other health workers (158.98) and doctors (264.57) (p < 0.001), other health workers and heads of the units (315.43) (p < 0.001), and other health workers and nurses (239.76) (p < 0.001); heads of the units and nurses (p = 0.023) (Figure 10).

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Social support: scores between doctors (213.57) and heads of the units (295.64) (p = 0.017) (Figure 11).

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Organizational support scores between heads of the units (332.00) and doctors (218.53) (p < 0.001), heads of the units and nurses (235.45) (p = 0.002), and heads of the units and other health workers (223.12) (p = 0.001) (Figure 12).

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Participation in decision making scores between heads of the units (295.64) and doctors (217.84) (p = 0.028) (Figure 13).

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Justice of reward scores between doctors (207.33) and heads of the units (292.10) (p = 0.012), and doctors and other health workers (259.78) (p = 0.018) (Figure 14).

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Manager feedback scores between heads of the units (308.00) and doctors (215.63) (p = 0.005) and heads of the units and nurses (223.57) (p = 0.009) (Figure 15).

The post hoc analysis revealed no statistically significant differences in Communication scores across occupational groups.

4. Discussion

The study aimed to find out which medical occupational group is the most exposed to stress and whether the perception of psychosocial risk determinants and the priorities of organizational intervention objects differ among medical staff holding different positions in the Lithuanian public healthcare institution.

The study findings suggest that the doctors’ group is the most exposed to work-related stress. Doctors experienced stress mainly due to high job demands, such as workload, overtime, tight deadlines, and responsibilities. In addition, doctors were dissatisfied with the institution’s efforts to ensure work–life balance, social support, organizational support, involvement in decision making, and justice of reward. This group also indicated a lack of managerial feedback. In line with the literature, the findings confirm that public sector doctors’ work is busier and more stressful than other occupation groups’ work, and this may lead to burnout and mental health problems [20,21,22,23]. For example, the Clinician Well-Being Collaborative provides a possible “antidote” to that by publishing materials and providing online information focused on leadership engagement, workload and workflow, resilience or constructive coping strategies, and work–life balance [24,25].

Nurses and other health workers were more stressed by role risk determinants: role overload (being under-skilled for a job) and unclear role. The results also confirm the findings of previous studies [26] and suggest that nurses and other health professionals face a conflict between their professional role expectations and work realities [27]. They also pointed out that organizational support did not fulfill their needs. Organizational support has a positive effect on workers’ performance and plays an important role in terms of their respect [28]. Other health professionals also indicated a lack of variety of tasks.

Heads of units emphasized only responsibility as a psychosocial risk and had no priorities concerning organizational intervention objects. These findings are not surprising, as heads of units are responsible for unit performances, and their work is largely administrative in nature.

The main limitations of this study are the cross-sectional nature of the study, limiting inferences of causality, and its dependence on self-reporting. Another limitation is that it did not include individual intervention objects, “whereas individual-level interventions focus on the problems and needs of individual workers (e.g., through counseling or therapy), organization-level interventions address the health and well-being of relatively large groups of workers in a uniform way (e.g., job redesign, training, and education)” [14]. Despite its limitations, this study supports participative problem-solving approaches because “employees are experts on their work and management of the work environment” [29].

5. Conclusions

The findings showed that different medical occupational groups in the same public healthcare institution highlighted different psychosocial risk determinants as causes of stress. The prioritization of the organizational intervention objects among these groups also differed. The study results suggest that focusing on the average worker does not have practical value and that it is important to understand the differential effects of different job characteristics on work outcomes considering occupational status while developing coping strategies in the institution. Finally, the findings suggest that public health care institutions should pay more attention to the working conditions of their doctors, in particular, to time pressure and work overload.