Determining Factors of Nurses’ Quality of Life Amidst the COVID-19 Pandemic

Abstract

Background

During the COVID-19 pandemic, nurses faced increased psychological burdens due to prolonged exposure to patients, resulting in diminished quality of life (QOL). Sense of coherence (SOC) has been recognized as a crucial psychological resource that enhances coping capacity and promotes QOL.

Objectives

This study aimed to examine the factors associated with QOL among nurses, particularly focusing on the role of SOC in mitigating job stress during the COVID-19 pandemic.

Methods

A cross-sectional survey was conducted among 798 staff nurses at a university hospital in Japan in December 2021 using four validated self-administered measures to assess SOC, job stress, social support, and QOL. Of the distributed questionnaires, 144 were returned (response rate: 18%). Multiple regression analyses were performed to explore factors predicting mental health-related QOL.

Results

Among nurses who cared for COVID-19 patients, job stress (β = 0.311, p = .018) and the manageability subscale of SOC (β = 0.282, p = .047) significantly predicted higher QOL scores. These associations were not observed among nurses who did not care for COVID-19 patients.

Conclusions

Enhancing the manageability aspect of SOC may be a protective factor against psychological stress in nurses caring for COVID-19 patients. These findings highlight the importance of localized psychological support strategies－such as structured peer support, resilience training, and organizational transparency－to help nurses maintain mental well-being and QOL during and beyond pandemic conditions.

Introduction

Since late 2019, the COVID-19 pandemic has imposed (World Health Organization [WHO], 2020) extraordinary and sustained pressures on healthcare systems worldwide and in Japan (Ministry of Health, Labour and Welfare [MHLW], 2021), with frontline nurses shouldering rapidly evolving clinical demands and prolonged close contact with patients. These conditions have led nurses to experience chronic fatigue and burnout (Buyo, 2022), raising concerns about their well-being and quality of life (QOL).

Although a substantial body of research has documented nurses’ mental health during the pandemic, comparatively fewer studies have examined health-related QOL, a broader construct that reflects functioning and well-being beyond symptomatology. Clarifying what shapes nurses’ mental health-related QOL under prolonged high-demand conditions is therefore essential for sustaining workforce capacity and for informing pragmatic support within hospitals.

Building on these concerns, this study investigates the factors that determine QOL, with particular attention to the role of sense of coherence (SOC)—a psychological resource linked to coping capacity—in a Japanese university hospital during the pandemic. We further consider whether these associations differ for nurses who directly cared for patients with COVID-19, with the goal of identifying targets for locally actionable, salutogenic interventions to maintain nurses’ well-being during and beyond pandemic conditions.

Below, we synthesize pre-and-intra-pandemic evidence on nurses’ mental health and QOL, outline SOC and Job Demand–Control–Support (JDCS) model, and identify gaps that motivate the present study.

Review of Literature

Pre-Pandemic Evidence on Nurses’ Mental Health and Quality of Life

Even before the COVID-19 pandemic, nurses were consistently exposed to high job demands that adversely affected mental health and health-related QOL. In this context, heavier workloads and reduced job control were linked to poorer psychological outcomes and lower health-related QOL. Studies linked heavier workloads and reduced job control with poorer psychological outcomes among nurses, highlighting the salience of organizational and role-related stressors (Chang et al., 2006; Khamisa et al., 2015; Mark & Smith, 2012). Also, nurses reported increased exposure to stress-related factors and lower health-related QOL because they are highly specialized interpersonal support service providers and are prone to stress reactions (Loibner et al., 2019). These findings established a pre-pandemic baseline in which work design and resources were central to nurses’ well-being.

COVID-19-Specific Stressors Among Nurses

The pandemic amplified existing pressures and introduced novel stressors. Frontline nurses encountered fear of infection and death of patients (Lorente et al., 2021), rapidly changing protocols, role redistribution (Vamvakas et al., 2022), and resource constraints such as an insufficient staffing (Lorente et al., 2021), high workload, role ambiguity, role conflict, role limitation (Shirali et al., 2021), and the nature of their job, which requires closer and longer contact with patients (Chatzittofis et al., 2021; Vamvakas et al., 2022). Elevated symptoms of nurses’ anxiety (12.1%) and depression (14.7%) were reported, requiring further assessment and treatment during successive waves (Sharma et al., 2021).

Quality of Life During the Pandemic

QOL declines were documented broadly not only among COVID-19-infected patients (Arab-Zozani et al., 2020), but also among those who merely feared infection, including elementary, junior, and high school students (Ohnishi, 2022) and local residents in epidemic-affected areas, including Wuhan and other cities in China (Ping et al., 2020).

For nurses, higher patient loads and communication challenges with patients were associated with lower health-related QOL even before COVID-19 (Oyama et al., 2015), suggesting that pandemic-era surges and complex care needs likely compounded risks. Nevertheless, compared with the extensive literature on stress and burnout, studies explicitly focusing on nurses’ QOL during COVID-19—particularly in Japan—have been relatively limited, leaving uncertainty about which factors most strongly shape mental health-related QOL under pandemic care conditions, which are expected to last for the next several years (Funato, 2021; Odagaki, 2020).

Sense of Coherence and its Relevance to Nurses’ Quality of Life

The SOC concept, developed by Antonovsky (Antonovsky, 1987), has proven to be one of the most influential factors in promoting people's QOL. The SOC is identified as:

a global orientation that expresses the extent to which one has a pervasive, enduring, though dynamic, feeling of confidence that (1) the stimuli deriving from one's internal and external environments in the course of living are structured, predictable, and explicable; (2) the resources are available to one to meet the demands posed by these stimuli; and (3) these demands are challenges worthy of investment and engagement (Antonovsky, 1993).

Across patient groups, higher SOC has been associated with better QOL, suggesting salutogenic pathways that help individuals mobilize resources and reframe stressors (Bonzanini et al., 2020; Gerasimcik-Pulko et al., 2009). Among nurses, it is reported that SOC may be considered as a major controlling factor for QOL (Kim & Choi, 2011).

Taken together, theory and evidence point to SOC as a plausible protective factor for nurses’ QOL amid sustained stress exposure. Few studies have examined the contribution of SOC to QOL.

Integrating Job Demand–Control–Support and Salutogenesis

The JDCS model posits that high demands coupled with low job control and insufficient social support elevate psychological strain (Johnson et al., 1989; Karasek, 1979). In pandemic nursing, demands surged while autonomy and support were often constrained. The JDCS framework suggests that such conditions increase the risk of adverse mental health outcomes. Additionally, Antonovsky's SOC theory provides a salutogenic perspective, emphasizing individuals’ capacity to perceive life as comprehensible, manageable, and meaningful. Therefore, SOC may be conceptualized as an internal resilience factor that may buffer the effects of work-related stress and contribute to better QOL. Integrating these two models allows for a comprehensive understanding of the mechanisms influencing nurses’ QOL during the pandemic.

Conceptual Framework for Quality of Life

Health-related QOL is typically positioned as a downstream outcome in comprehensive models linking clinical factors, symptoms, functional status, and patients’ appraisal (Wilson & Cleary, 1995). Within hospital work settings, the JDCS model posits that high demands combined with constrained autonomy and insufficient support increase psychological strain, ultimately lowering QOL (Karasek, 1979). In parallel, salutogenic theory conceptualizes SOC as a generalized resistance resource that shapes appraisal of stressors and mobilization of coping resources (Antonovsky, 1987). Consistent with the social support–buffering and conservation-of-resources perspectives, higher SOC and stronger perceived support should mitigate the adverse impact of job stress on QOL (Cohen & Wills, 1985; Hobfoll, 1989).

Guided by this integrative framework, we expected that (a) greater job stress would be associated with lower mental health-related QOL; (b) higher SOC would be associated with higher QOL; and (c) greater social support would be positively related to QOL and could attenuate the association between job stress and QOL. Because direct COVID-19 care constituted an exceptional demand context, we further anticipated that these associations might be more pronounced among nurses who directly cared for patients with COVID-19.

Gap and Rationale for the Present Study

Despite substantial documentation of stressors during COVID-19, fewer studies have tested how SOC interacts with job stress to shape QOL among nurses, and evidence from Japanese university hospitals remains scarce. Moreover, whether determinants of QOL differ between nurses who did versus did not care for COVID-19 patients are unclear. Addressing these gaps, this study examines the factors that determine QOL including relative contributions of job stress, SOC (and its subscales), and social support among nurses, with stratification by COVID-19 care experience in a large Japanese university hospital setting. As negative effects on psychological health are often broader and long-lasting than those on physical health (Allsopp et al., 2019), this study focused on the mental health-related QOL of nurses.

Methods

This study aimed to identify factors affecting mental health-related QOL among nurses during the COVID-19 pandemic, with a particular focus on the role of SOC. Based on prior evidence suggesting prolonged psychological burden on nurses caring for COVID-19 patients, we hypothesized that SOC contributes positively to QOL. A cross-sectional quantitative survey was conducted to examine these relationships.

Participants

A questionnaire survey was distributed in December 2021, during the period between the fifth and sixth waves of the COVID-19 pandemic in Japan. The eligible participants were staff nurses working in a university hospital in Japan (N = 798). Nurse managers were not included in this study because they usually do not provide direct patient care (Oyama et al., 2015). Questionnaires were delivered to nurse managers via hospital nurse directors for distribution. Participants answered the questionnaire voluntarily and anonymously. After completing the questionnaires, they were sealed and returned by mail. Informed consent was obtained from all participants in this study. An informed consent sheet was attached to the questionnaire, and each participant was free to decline participation without any penalty. The participants’ consent was confirmed by the return of the questionnaire. Ethical approval for this study was granted by the Biomedical Sciences Ethics Board of Nagasaki University, Japan (Approval No. 21101408). Inclusion and exclusion criteria were defined as follows.

Inclusion Criteria

• - Nurses working at the university hospital during the survey period (December 2021).

• - Nurses engaged in direct patient care.

• - Nurses who agreed to participate in the study by returning the completed questionnaire via the provided return envelope. The act of returning the anonymous questionnaire was considered to imply informed consent.

Exclusion Criteria

• - Nurse managers, including the Director of Nursing, who did not routinely engage in direct patient care.

Sample Size Justification and Sensitivity (G*Power)

An a priori power analysis was conducted in G*Power 3.1.9.7 (F tests; Linear multiple regression: Fixed model, R² deviation from zero; two-tailed; α = .05; power = .80). Assuming f² = 0.10 with k = 5 predictors, the required total sample size was N = 134. Our final sample (N = 144) exceeded this target. We also performed a sensitivity analysis fixing N = 144, α = .05, power = .80, and k = 5, which indicated a minimum detectable effect of f² = 0.093. The final model included five predictors: gender, age, job stress, social support, and SOC scores.

The participant selection process is summarized in Figure 1, including invitations, responses, exclusions, and the final analytic sample (n = 144).

Figure 1.

STROBE Flow Diagram Of Participant Selection.

Measures

The Japanese version of each scale, culturally adapted and psychometrically validated in previous studies, was used in this study.

Participants’ SOC was measured using the Japanese version of Antonovsky's short 13-item questionnaire (Yamazaki, 1999). SOC consists of three subscales: comprehensibility, manageability, and meaningfulness. Sense of comprehensibility refers to the degree to which life events make sense and are understandable to people. Manageability is the extent to which people perceive that they have sufficient available (internal and external) resources to satisfy their needs. The sense of meaningfulness represents the source of motivation, namely, the extent to which people feel that life has emotional meaning and problems faced are challenges rather than hindrances (Antonovsky, 1993). Each item was scored on a scale from 1 to 7, and a higher score implied more successful management of stressors to maintain and improve well-being. The validity and reliability of this scale have been previously confirmed (Endo et al., 2013).

Job stress was measured using 17 items from the Japanese version of the National Institute for Occupational Safety and Health (NIOSH) Job Stress Questionnaire (Shimomitsu, 1998). The validity and reliability of the scales used in the questionnaire have been previously confirmed (Centers for Disease Control and Prevention [CDC], 2023). According to the Centers for Disease Control and Prevention (Shimomitsu, 2000), job stress can be defined as harmful physical and emotional responses that occur when the requirements of the job do not match the capabilities, resources, or needs of the worker, which can lead to poor health and even injury. The scale consists of nine dimensions: psychological workload (quantity), psychological workload (quality), perceived physical workload, interpersonal stress at work, stress due to work environment, degree of control over work, degree of utilization of one's own skills, degree of perceived job aptitude, and job satisfaction. Of these 17 items, three comprising the dimension of interpersonal stress at work were excluded because they were deemed statistically inadequate. Each remaining item was scored on a scale from 1 to 4; some reversal items were reversed and then added up. Higher scores indicated lower stress burden.

Social support was measured using nine items from the Japanese version of the NIOSH Job Stress Questionnaire (Shimomitsu, 1998). This scale consists of three dimensions: social support from supervisors; social support from others at work; and social support from spouse, family, and friends. Each item was scored on a scale from 1 to 4 and summed. Lower scores indicated greater support. The validity and reliability of this scale have been confirmed in previous studies (Inoue et al., 2014).

Participants’ mental and physical health-related QOL was measured using the Japanese version of the Short-Form Health Survey (SF-8) (Fukuhara & Suzukamo, 2001). The validity and reliability of this scale have been confirmed (Fukuhara & Suzukamo, 2001). The SF-8 consists of eight items: vitality, social function, mental health, role-emotional, general health, physical function, role-physical, and bodily pain. SF-8 can be divided into two groups: physical component summary (PCS) and mental component summary (MCS). In this study, MCS was used as the dependent variable. The individual items comprising the MCS were reversed and summed. Higher scores indicated higher QOL. An overview of the measurement instruments used in this study, including the number of items and subscales, is presented in Table 1.

Table 1.

Summary of Measures Used in the Study.

Scale	Purpose	Number of items	Subscales/Components	Reference
SOC-13 (Japanese version)	Measure sense of coherence	13	Comprehensibility, Manageability, Meaningfulness	Antonovsky (1987, 1993); Yamazaki (1999); Endo et al. (2013)
Job Stress (NIOSH/Japanese version)	Assess occupational stress	17	Job demands, Job control, Interpersonal conflict, etc.	Shimomitsu (1998, 2000)
Social Support (NIOSH/Japanese version)	Measure perceived social support	9	Supervisor support, Coworker support, Family and friend support	Shimomitsu (1998, 2000)
SF-8(Japanese version)	Assess mental and physical health-related QOL	8	Social function, mental health, role-emotional, etc.	Fukuhara & Suzukamo (2001)

Note. SOC, sense of coherence; NIOSH, National Institute for Occupational Safety and Health; SF-8, Short-Form Health Survey; QOL, quality of life.

Anxiety about the COVID-19 pandemic's sixth wave was measured by a one-item original scale: “How anxious are you now regarding the upcoming sixth wave of the COVID-19 pandemic?” The participants were required to answer using a 5-point scale estimate, with 1 meaning “not anxious at all” and 5 meaning “very anxious.” The original scale was used in the analysis while examining correlations with other variables.

Sociodemographic information included gender, age, educational background, length of time working as nurses, professional qualification (e.g., certified nurse, certified nurse specialist), marital status, dominant status (whether or not they live with other people), employment status (full- or part-time), hospital unit, whether or not they had cared for COVID-19 patients, whether or not they were infected with COVID-19, whether or not their family members’ were infected with COVID-19, and anxiety about the COVID-19 pandemic, which was expected as the sixth wave.

Statistical Analysis

This study was a descriptive quantitative cross-sectional survey, and IBM SPSS Statistics Version 28.0 was used to calculate descriptive statistics for the participants’ general characteristics. The participants were divided into two groups: those who had cared for COVID-19 patients (Group A) and those who had not (Group B). The following statistical analyses were performed according to group. The scores of the scales (SOC, job stress, social support, PCS, and MCS), anxiety about COVID-19, and age were expressed as mean and standard deviation, and differences between the different groups were analyzed by performing t-tests or Mann–Whitney U tests. Prior to selecting statistical tests, the distribution of scale scores was visually inspected using histograms. Based on this assessment, appropriate parametric or non-parametric tests were chosen accordingly. Spearman's correlation coefficients were calculated to determine the associations between dependent (MCS) and independent variables (age, anxiety about COVID-19, SOC, job stress, social support, and PCS). Multiple regression analysis was performed to explore the factors associated with mental health-related QOL between Groups A and B. Missing data were addressed by using mean substitution for continuous variables where appropriate. The extent of missing data was minimal and did not affect the main analysis. All tests were two-tailed, with a significance level of p < .05.

Bias

To minimize selection bias, all staff nurses working in the university hospital during the study period were invited to participate, regardless of department or shift. Nurse managers not directly involved in patient care were excluded based on predefined criteria. To reduce information bias, participants completed the questionnaire anonymously and voluntarily, without pressure from supervisors or colleagues. Additionally, the self-administered nature of the questionnaire ensured privacy and reduced the likelihood of socially desirable responses.

This cross-sectional study was reported in accordance with the STROBE guidelines.

Results

A total of 144 questionnaires were anonymously returned by the respondents (response rate: 18.0%). This size exceeded the required number identified in the power analysis, indicating that the study had sufficient statistical power to detect medium-sized effects. Table 2 shows the participants’ characteristics. The majority of respondents were female (91%), and a few had experienced COVID-19 infection (1.4%), while 36% had cared for patients with COVID-19.

Table 2.

Participants’ Characteristics.

Variables	n	(%)
Age
≧45	33	22.9
35–44	39	27.1
25–34	59	41.0
＜ 25	13	9.0
Gender
Male	13	9.0
Female	131	91.0
Educational background
Graduate school	12	8.4
University school of nursing/College of nursing	65	45.5
Nursing specialty school/Nursing short-term school	66	46.2
Experience in nursing
≧15	54	37.5
10–14	25	17.4
5–9	25	17.4
＜ 5	40	27.8
Professional qualification
Having	7	4.9
Not having	137	95.1
Marital status
Married	59	41.3
Single	84	58.7
Cohabitation status
With someone	93	65.0
With no one	51	35.0
Employment status
Full-time	141	97.9
Part-time	3	2.1
Department/Unit
Ward nurse	102	71.3
Outpatient nurse	41	28.7
COVID-19 patient care experience
Experienced (Group A)	53	36.8
Never (Group B)	91	63.2
One's own experience with COVID-19 infection
Experienced	2	1.4
Never	142	98.6
One's family members’ experience with COVID-19 infection
Experienced	2	1.4
Never	142	98.6

Note. N = 144.

The mean meaningfulness (range: 7–28), comprehensibility (range: 9–31), manageability (range: 9–25), job stress (range: 18–46), and social support (range: 9–32) scores were 18.7 (SD = 4.20), 20.8 (SD = 4.60), 16.7 (SD = 3.61), 31.8 (SD = 4.98), and 17.6 (SD = 5.06), respectively. The average PCS score was 51.2 (SD = 4.93), with a range of 29–67. The average MCS score was 51.1 (SD = 7.61), with a range of 28–59. Regarding the scales used in the study, Cronbach's alpha was 0.819 for SOC (0.753 for meaningfulness, 0.669 for comprehensibility, and 0.501 for manageability), 0.746 for job stress, 0.900 for social support (0.709 for supervisors, 0.752 for others at work, and 0.758 for spouse/family/friends), 0.641 for PCS, and 0.842 for MCS.

Bivariate Analysis

The correlation coefficients between MCS and age, anxiety about COVID-19, SOC, job stress, social support, and PCS scores are examined. The scores for Group A showed stronger correlations between MCS and all variables, except age, anxiety about COVID-19, and social support from supervisors. The strongest correlation was found between MCS and SOC (ρ=0.599, p < .01). Among the three subscales of SOC, manageability had the strongest correlation with MCS (ρ=0.527, p < .01), followed by comprehensibility (ρ=0.478, p < .01) and meaningfulness (ρ=0.461, p < .01). For social support, the strongest correlation was found between MCS and support from others at work (ρ=-0.422, p < .01), followed by support from spouse/family/friends (ρ=-0.373, p < .01). In contrast, the scores for Group B showed few correlations between MCS and all other variables.

Table 3 shows the comparisons of demographic variables among participants between Groups A and B. Significant differences were observed between the two groups in terms of age, social support, PCS, and MCS. However, there were no significant differences between the two groups in terms of anxiety about COVID-19, job stress, or SOC scores. Social support, including the three subscales, obtained significantly lower scores in Group A (p = .004). For each subscale, support from supervisors (p = .016), support from others at work (p = .009), and support from spouse/family/friends (p = .041) had significantly lower scores than in Group B. Furthermore, both PCS and MCS had significantly lower scores in Group A (p < .001) than in Group B (p < .001).

Table 3.

Comparisons of Demographic Variables Among Participants Between Group A and B.

	COVID-19 patient care experience
Variables	Group A	Group B	p-value
Age	32.2 (8.35)	38.3 (10.8)	<0.001
(Range: 23–63)
Anxiety about COVID-19(sixth wave)	3.85 (0.74)	3.90 (0.87)	0.636
(Range: 2–5)
SOC score
Total score	57.3 (10.3)	55.5 (10.2)	0.307
(Range: 31–77)
Meaningfulness	18.7 (4.55)	18.7 (4.00)	0.983
(Range: 7–28)
Comprehensibility	21.3 (4.18)	20.5 (4.82)	0.424
(Range: 9–31)
Manageability	17.3 (3.63)	16.3 (3.56)	0.080
(Range: 9–25)
Job stress	40.5 (5.14)	40.6 (5.97)	0.776
(Range: 25–56)
Social supports
Total scores	16.0 (5.03)	18.5 (4.87)	0.004
(Range: 9–32)
From supervisors	6.23 (2.15)	7.16 (2.02)	0.016
(Range: 3–11)
From others at work	5.43 (2.21)	6.37 (1.98)	0.009
(Range: 3–11)
From spouse/family/friends	4.34 (1.80)	4.96 (1.93)	0.041
(Range: 3–12)
PCS	17.4 (3.04)	19.2 (1.97)	<0.001
(Range: 7–21)
MCS	14.7 (3.28)	18.0 (2.17)	<0.001
(Range: 6–20)

Note. SOC, sense of coherence; COVID-19, coronavirus disease 2019; PCS, physical component summary score; MCS, mental component summary score.

Values are expressed as mean (standard deviation) in this table.

Multivariate Analysis

Table 4 shows the determinants of MCS in Groups A. Job stress was the strongest determinant of MCS (β = 0.311, p = .018), followed by manageability (β = 0.282, p = .047). Lower job stress indicated higher MCS. Furthermore, higher manageability scores indicated higher MCS scores. However, this tendency was not observed in Group B.

Table 4.

Hierarchical Multilevel Regression Models to Predict Nurses’ MCS (Group A).

	MCS
	Model 1		Model 2
Items	Beta	p-value	Beta	p-value
Sex	-.137	.284	-.050	.668
Age	-.060	.641	-.083	.503
Job stress	.394	.006	.311	.018
Social supports
From others at work	-.233	.110	-.096	.471
SOC
Meaningfulness			.136	.390
Comprehensibility			.146	.328
Manageability			.282	.047
AdjR2	.251	.001	.418	<.001

Note. MCS, mental component summary score; SOC, sense of coherence; AdjR², adjusted R-squared.

To identify the factors of each subscale of SOC contributing to MCS, Group A was further analyzed by performing hierarchical multiple regression analysis through the following procedures.

First, manageability was identified as the only factor that was significantly associated with MCS in the three SOC subscales when the forced entry method was applied in the regression analysis (Table 4, Model 2). Second, meaningfulness (Table 5, Model 3) and comprehensibility (Table 5, Model 5) were entered exclusively into the regression model. Third, meaningfulness and manageability were entered to probe the efficiency of manageability over meaningfulness (Table 5, Model 4). Fourth, comprehensibility and manageability were entered to probe the efficiency of manageability (Table 5, Model 6).

Table 5.

Hierarchical Multilevel Regression Models to Predict Nurses’ MCS (Group A).

	MCS
	Model 3		Model 4		Model 5		Model 6
Items	Beta	p-value	Beta	p-value	Beta	p-value	Beta	p-value
Gender	-.106	.376	-.076	.505	-.042	.727	-.045	.699
Age	-.170	.185	-.096	.437	-.060	.616	-.042	.715
Job stress	.288	.037	.318	.016	.331	.013	.339	.008
Social supports
from others at work	-.116	.414	-.099	.457	-.153	.261	-.121	.352
SOC
meaningfulness	.376	.008	.190	.201
comprehensibility					.369	.004	.191	.173
manageability			.335	.012			.316	.021
AdjR2	.344	<.001	.419	<.001	.362	<.001	.422	<.001

Note. MCS, mental component summary score; SOC, sense of coherence; AdjR², adjusted R-squared.

Meaningfulness significantly determined MCS when it was solely entered as an independent variable (β=0.376, p = .008). However, when it was included together with manageability, manageability was identified as the only factor that significantly determined MCS (β=0.335, p = .012). While comprehensibility significantly determined MCS when it was solely entered (β=0.369, p = .004), when it was entered together with manageability, manageability was a factor that significantly determined MCS (β=0.316, p = .021).

Discussion

This study indicated that no factors significantly determined the MCS of nurses who had no experience caring for COVID-19 patients. On the other hand, the MCS of nurses who had cared for COVID-19 patients was determined by job stress and SOC. This finding suggests that direct patient care under pandemic conditions imposes unique psychological burdens, which may require tailored support strategies.

This study indicated that the factors contributing to MCS are diverse due to engagement with COVID-19 patient care. Such diversity can be caused by nurses’ characteristics of seeking social support when needed. It was reported that nurses who engaged in caring for COVID-19 patients often suffered from emotional stressors, such as anxiety regarding unfamiliar working environments and processes, depression due to unsuccessful treatment of critically ill patients, and worry about their families (Shen et al., 2020). These findings highlight the need for hospital administrators and policymakers to implement structured interventions to mitigate job stress and enhance SOC among frontline nurses. For example, programs that promote manageability—such as peer support, structured mentoring, and accessible mental health services—may serve as effective buffers against psychological distress (Labrague, 2021).

In this study, Group A, which cared for COVID-19 patients, successfully obtained social support from colleagues and spouses/family/friends. Social support works as a part of general resistance resources (Antonovsky, 1993). Therefore, it may help maintain the level of QOL. Furthermore, social support reduces anxiety about COVID-19 (Labrague & De los Santos, 2020; Xiao et al., 2020), as well as stress reactions (Shen et al., 2020). It also promotes sleep quality (Xiao et al., 2020) and psychological resilience (Kılınç & Sis Çelik, 2021). Therefore, social support may raise QOL, as demonstrated in a previous study (Ebrahimi et al., 2021).

One of the factors that significantly contributed to MCS in Group A was job stress. Although the job stress scores were not significantly different between Groups A and B, job stress was identified as a significant determinant of MCS in each model of Group A. This can be interpreted as Group A nurses being exposed to unique stressors when caring for COVID-19 patients. Nurses must prevent the spread of infection to themselves and others under the strict biosecurity measures of the institution (Said & El-Shafei, 2021). Under these conditions, nurses are forced to wear personal protective equipment, multiple glove layers, face shields, and fully ventilated suits. This uncomfortable outfit may raise body temperature and result in heat stress and liquid loss (Loibner et al., 2019).

To cope with work-related stress, nurses in Group A may efficiently apply SOC. As shown in Table 5, manageability was the most effective determinant of MCS among the three SOC subscales. Manageability is the degree to which one feels that there are formal and informal resources at one's disposal that can be used to meet the requirements of the stimuli with which one is bombarded. Formal resources include social services and care staff in public and private organizations. Informal resources include family, circles of friends, colleagues, and significant others (Eriksson & Mittelmark, 2017). During the pandemic, nurses should comply with strict regulations against infection, particularly those who care for COVID-19 patients. Therefore, manageability (i.e., the ability to be able to feel that one has formal and informal resources and deal with various stressors) is essential for nurses to combat stress. One can assume that Group A nurses employed manageability to cope with work-related stress, utilizing social support as an informal resource, which results in promoting the level of MCS.

The present study indicated that manageability is key to promoting the QOL of nurses who care for COVID-19 patients. Table 4 shows that manageability was the strongest factor of MCS among the three subscales in Model 2. However, manageability is not an exclusive factor that contributes to MCS. Comprehensibility (Table 5, Model 5) and meaningfulness (Table 5, Model 3) also contributed to MCS. It was pointed out that the three SOC subscales are strongly related to each other (Eriksson & Mittelmark, 2017), which is statistically supported in this study. While previous studies have identified general stressors during the COVID-19 pandemic (Buyo, 2022), few have explored how internal psychological resources such as SOC interact with job stress to shape QOL specifically among nurses in Japan. This research fills that gap by providing empirical evidence from a large university hospital context.

Therefore, it can be said that to promote MCS, it is essential to raise not only the level of manageability, but also that of comprehensibility and meaningfulness. One can suggest that repetitive positive experiences in caring for patients, such as witnessing the patients’ recovery and receiving appreciation from patients and their families (Nakano & Aoyama, 2007), may increase nurses’ SOC. Developing a working environment in which nurses can openly speak about work procedures and provide opportunities for nurses to work autonomously may also help nurses in increasing SOC (Yamazumi & Yasukata, 2011).

The findings of this study, particularly the significant role of manageability of SOC and social support in maintaining nurses’ QOL, are consistent with the assumptions of the JDCS model. This model posits that high job demands, when coupled with low control and insufficient support, lead to increased psychological burden. In the context of this study, manageability may function similarly to job control, acting as a personal resource that enables nurses to effectively cope with stress. Social support, likewise, serves as a protective factor. Based on these theoretical underpinnings, practical interventions such as structured peer support programs, targeted resilience training, and enhanced organizational transparency could help bolster manageability and perceived control, thereby improving nurses’ mental health outcomes during and beyond the pandemic.

Why group differences were not significant for COVID-19 anxiety, job stress, and SOC.

Although nurses who cared for patients with COVID-19 showed lower QOL (PCS/MCS) than those who did not, we observed no significant between-group differences in anxiety about the sixth wave, job stress, or SOC. Several contextual and methodological factors can account for these null findings. First, the study was conducted between the fifth and sixth waves in a single university hospital operating under standardized infection-control policies, which likely homogenized exposure and coping resources across units, reducing between-group variability. Second, the region's relatively low infection rates during the survey period may have tempered differential anxiety attributable to direct COVID-19 care. Third, the comparison used a binary exposure (“ever” vs. “never” direct COVID-19 care); this coarse classification does not capture dose (duration, intensity, unit type) and may attenuate true differences. Fourth, measurement constraints may have contributed: a single-item anxiety indicator provides limited reliability, and potential range restriction/ceiling effects in psychosocial scales can further shrink detectable contrasts. Fifth, the split between groups was unbalanced (approximately one-third vs. two-thirds), limiting power for detecting small mean differences. Taken together, the absence of statistical significance should not be over-interpreted as equivalence; rather, it likely reflects restricted variability and conservative measurement, while the lower QOL in the COVID-care group indicates downstream impacts were present even if proximal predictors (anxiety, stress, SOC) did not differ at the mean level. Future work that measures care intensity, adopts multi-item anxiety scales, and applies equivalence/TOST testing will better adjudicate small between-group differences.

Hypotheses Appraisal

Hypothesis (a) was supported in the COVID-care group: higher job stress (given our coding, lower scores indicate higher stress) was associated with lower MCS in multivariable models. Hypothesis (b) was also supported, with SOC—particularly the manageability facet—emerging as a consistent determinant of MCS. For Hypothesis (c), while perceived social support correlated with MCS, the buffering effect (job stress × support) was not formally tested and thus remains inconclusive; future studies should examine this interaction explicitly and consider intensity/duration of COVID-19 care.

Strengths and Limitations

A limitation of this study was the survey's low response rate. Therefore, response bias may exist. As the present study was cross-sectional, we could not prove any causality between the independent variables and MCS. This study also requires an investigation of the physical dimensions of QOL. We prioritized the investigation of the structure of nurses’ MCS. However, the physical dimensions of QOL are related to activities of daily living and vitality (Imaida & Fukui, 2022). Therefore, PCS must be considered a physical and biochemical GRRs (Antonovsky, 1979), which may also contribute to nurses’ QOL.

This study was conducted at a single university hospital in Japan. However, similar findings were reported in a study conducted at another university hospital in a different region of Japan, where nurses who cared for COVID-19 patients exhibited higher levels of stress (Daito & Hanaki, 2022). In Japan, infection-control manuals are standardized and regularly revised by national authorities, and hospitals are expected to follow these guidelines. Therefore, the stress related to caring for infected patients and implementing infection-control measures may be similar across institutions. Furthermore, both hospitals were large-scale institutions with over 500 beds, suggesting that the stress burden during the COVID-19 pandemic was comparable. Nonetheless, generalizability remains limited due to the study's single-site design.

Regional differences may also have influenced the results. The region examined had relatively low infection rates during the study period (MHLW, 2023). Previous studies have reported that anxiety related to infection can vary by region and may affect QOL (Huang et al., 2020). In rural communities, where interpersonal relationships are tightly knit, the spread of misinformation regarding infection can increase anxiety. These contextual factors may limit the applicability of the findings to other settings.

Despite these limitations, this study has several strengths. First, we used culturally adapted and psychometrically supported instruments for SOC, job stress, social support, and QOL (SF-8 MCS), enhancing measurement validity. Second, an a priori power analysis was conducted and the final sample (N = 144) exceeded the required size, reducing the risk of type II error. Third, reporting followed STROBE recommendations and the analytic plan distinguished nurses with and without direct COVID-19 care, revealing patterns that might be obscured in pooled analyses. Finally, we increase transparency and reproducibility by providing a de-identified dataset and data dictionary in an open repository, enabling verification and secondary analysis.

Implications for Practice

This study highlights the importance of providing psychological support to nurses who are involved in infection control and patient care during pandemics, even in regions with relatively low infection rates. Institutional measures should include regular mental health assessments and the provision of stress-reduction resources tailored to regional and institutional contexts. Furthermore, given that stressors may be amplified by social dynamics in rural areas, health administrators and policymakers should consider region-specific strategies to mitigate stigma and misinformation, thereby promoting nurses’ psychological well-being and QOL.

Moreover, institutions should consider implementing structured peer support programs and targeted resilience training, both of which can enhance manageability—a key component of the SOC. These strategies, grounded in the JDCS and SOC frameworks, can help mitigate job-related psychological strain and improve nurses’ QOL, particularly in high-demand, low-control work environments.

Conclusion

This study demonstrated that the MCS of nurses who engage in caring for COVID-19 patients was determined by job stress and SOC, particularly manageability. Therefore, enhancing manageability is essential for maintaining nurses’ QOL.

Given that manageability reflects a nurse's perceived ability to mobilize both formal and informal resources, practical interventions such as structured peer support, targeted resilience training, and organizational transparency should be considered to strengthen this domain.

These findings are consistent with previous studies suggesting that improving SOC contributes to mental well-being in healthcare professionals (Labrague, 2021).

Thus, nurse administrators and policymakers should prioritize workplace environments that facilitate access to psychological support and promote personal coping competencies, especially in settings involving infectious disease management.

Appendix

Section I: About Yourself

• (1) What is your gender? Please select one.

  • 1. Male
  • 2. Female
  • 3. Prefer not to answer
• (2) How old are you?

  • (______) years old
• (3) What is your highest level of education? Please select one.

  • 1. Graduate school
  • 2. University (Nursing Faculty, 4-year program)
  • 3. Nursing school or junior college (3-year program), or integrated program
• (4) How many total years have you worked as a nurse?

  • (______) years
• (5) Do you have any professional certifications, such as Certified Nurse or Certified Nurse Specialist?

  • 1. Yes
  • 2. No
• (6) What is your current marital status?

  • 1. Married (including common-law marriage)
  • 2. Single (including divorced, never married, widowed)
• (7) Do you currently live with someone?

  • 1. Yes
  • 2. No
• (8) If you answered “Yes” to question (7), who do you live with? (You may select more than one.)

  • 1. Spouse/Partner
  • 2. Child(ren)
  • 3. Parent(s), including in-laws
  • 4. Others
• (9) How anxious are you now about the possibility of a “6th wave” of COVID-19 infections?

  • 1. Very anxious
  • 2. Somewhat anxious
  • 3. Neutral
  • 4. Not very anxious
  • 5. Not anxious at all

Section II: About Your Hospital

• (1) What is your current employment status?

  • 1. Full-time
  • 2. Part-time
  • 3. Temporary/Dispatch
• (2) Are you currently working mainly in the ward or outpatient department?

  • 1. Ward
  • 2. Outpatient
• (3) Are you currently providing care to COVID-19 patients, or have you done so in the past?

  • 1. Currently providing care
  • 2. Provided care in the past
  • 3. Never provided care

Section III: COVID-19 Situation Around You

• (1) Have you ever been infected with COVID-19?

  • 1. Yes
  • 2. No
• (2) Has any of your family ever been infected with COVID-19?

  • (1) Yes
  • (2) No

Section Ⅳ: SOC-13

• (1) Do you have the feeling that you don’t really care about what goes on around you?

   (1 = very seldom or never – 7 = very often)

• (2) Has it happened in the past that you were surprised by the behavior of people whom you thought you knew well?

   (1 = never happened – 7 = always happened)

• (3) Has it happened that people whom you counted on disappointed you?

   (1 = never happened – 7 = always happened)

• (4) Until now your life has had:

   (1 = no clear goals or purpose at all – 7 = very clear goals and purpose)

• (5) Do you have the feeling that you are being treated unfairly?

   (1 = very often – 7 = very seldom or never)

• (6) Do you have the feeling that you are in an unfamiliar situation and don’t know what to do?

   (1 = very often – 7 = very seldom or never)

• (7) Doing the things you do every day is:

   (1 = a source of deep pleasure and satisfaction – 7 = a source of pain and boredom)

• (8) Do you have very mixed-up feelings and ideas?

   (1 = very often – 7 = very seldom or never)

• (9) Does it happen that you have feelings inside you would rather not feel?

   (1 = very often – 7 = very seldom or never)

• (10) Many people – even those who are strong/resilient – sometimes lose hope in certain situations. How often have you lost hope in the past?

   (1 = never – 7 = very often)

• (11) When something happened, have you generally found that:

   (1 = you overestimated or underestimated its importance – 7 = you saw things in the right proportion)

• (12) How often do you have the feeling that there's little meaning in the things you do in your daily life?

   (1 = very often – 7 = very seldom or never)

• (13) How often do you have the feelings that you’re not sure you can keep under control?

   (1 = very often – 7 = very seldom or never)

Section Ⅴ: Job Stress

• (1) I have to do an excessive amount of work.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (2) I can’t complete all of my work in the allotted time.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (3) I have to work very hard.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (4) My job requires a great deal of concentration

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (5) My job requires a high level of skill.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (6) I must keep my mind on my work at all times.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (7) My job requires a lot of physical effort.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (8) I can work at my own pace.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (1) I have a lot to say about how my job is done.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (2) I have a lot of influence over the decisions made by my work group.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (3) My job doesn’t make good use of my skills and abilities.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (9) (12) There are many arguments and conflicts in my work group.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (4) There is a lot of friction between my work group and other groups.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (5) The people I work with take a personal interest in me.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (6) The physical working conditions at my job are bad (e.g., noise, temperature, lighting).

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (10) (16) My job is a good fit for me.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

• (11) (17) My job is meaningful.

  (1 = Strongly Agree 2 = Slightly Agree 3 = Slightly Disagree 4 = Strongly Disagree)

Section VI: Social Support

• (1) How easy is it to talk with each of the following people?

  • 1. Your immediate supervisor (boss)
  (1 = Very Much 2 = Somewhat 3 = Not At All 4 = Don’t Have Any Such Person)

  • 2. Other people at work
  (1 = Very Much 2 = Somewhat 3 = Not At All 4 = Don’t Have Any Such Person)

  • 3. Your spouse, friends and relatives

  (1 = Very Much 2 = Somewhat 3 = Not At All 4 = Don’t Have Any Such Person)

• (2) How much can each of these people be relied on when things get tough at work?

  • 1. Your immediate supervisor (boss)
  (1 = Very Much 2 = Somewhat 3 = Not At All 4 = Don’t Have Any Such Person)

  • 2. Other people at work
  (1 = Very Much 2 = Somewhat 3 = Not At All 4 = Don’t Have Any Such Person)

  • 3. Your spouse, friends and relatives

  (1 = Very Much 2 = Somewhat 3 = Not At All 4 = Don’t Have Any Such Person)

• (3) How much is each of the following willing to listen to your personal problems?

  • 1. Your immediate supervisor (boss)
  (1 = Very Much 2 = Somewhat 3 = Not At All 4 = Don’t Have Any Such Person)

  • 2. Other people at work
  (1 = Very Much 2 = Somewhat 3 = Not At All 4 = Don’t Have Any Such Person)

  • 3. Your spouse, friends and relatives

  (1 = Very Much 2 = Somewhat 3 = Not At All 4 = Don’t Have Any Such Person)

Section VII: Health-related Quality of Life

• (1) Overall, how would you rate your health during the past 4 weeks?

  (1 = Excellent 2 = Very good 3 = Good 4 = Fair 5 = Poor 6 = Very poor)

• (2) During the past 4 weeks, how much did physical health problems limit your physical activities (such as walking or climbing stairs)?

  (1 = Not at all 2 = A little bit 3 = Somewhat 4 = Quite a bit 5 = Could not do physical activities)

• (3) During the past 4 weeks, how much did physical health problems interfere with your usual activities (such as working or taking care of things at home)?

  (1 = Not at all 2 = A little bit 3 = Somewhat 4 = Quite a bit 5 = Could not do usual work)

• (4) During the past 4 weeks, how much bodily pain have you had?

  (1 = No pain at all 2 = Very mild pain 3 = Mild pain 4 = Moderate pain 5 = Severe pain 6 = Very severe pain)

• (5) During the past 4 weeks, how much energy did you have?

  (1 = Very much energetic 2 = Quite a bit energetic 3 = Somewhat energetic 4= A little energetic 5 = Not at all energetic)

• (6) During the past 4 weeks, how much did physical or emotional problems interfere with your normal social activities with family or friends?

  (1 = Not at all 2 = A little bit 3 = Somewhat 4 = Quite a bit 5 = Unable to socialize)

• (7) During the past 4 weeks, how much have you been bothered by emotional problems (such as feeling anxious, depressed, or irritable)?

  (1 = Not at all bothered 2 = Slightly bothered 3 = Somewhat bothered 4 = Considerably bothered 5 = Extremely bothered)

• (8) During the past 4 weeks, how much did emotional problems interfere with your normal work (including work outside the home and at home)?

  (1 = Not at all bothered 2 = Slightly bothered 3 = Somewhat bothered 4 = Considerably bothered 5 = Unable to carry out daily activities)