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. 2025 Jan 16;81(9):5858–5869. doi: 10.1111/jan.16709

Psychological Empowerment as a Mediator of Effort–Reward Imbalance Effects on Burnout, Anxiety and Depression Among Nursing Home Professionals: A Cross‐Sectional Study

Amélie Bouche 1,✉, Jacques Jaussaud 2, Océane Pic 1, Michèle Koleck 1, Lucile Dupuy 1, Camille Ouvrard 1, Hélène Amieva 1, Nicole Rascle 1
PMCID: PMC12371791  PMID: 39817661

ABSTRACT

Aims

Few studies have explored empowerment as a predictor of mental health outcomes in geriatric healthcare professionals. This research addresses this gap by using the ‘effort‐reward imbalance’ theory of work‐related stress to develop a comprehensive model, examining the role of psychological empowerment in the psychological outcomes of nursing home professionals.

Design

This cross‐sectional exploratory study used structural equation modelling (SEM) to test a model on the mediating role of psychological empowerment in the relationship between effort–reward ratio and burnout, anxiety and depression.

Methods

From 2021 to 2023, we used convenience sampling to enrol 280 physicians, nurses and other healthcare workers from 13 nursing homes in France. Self‐administered measurements included Psychological Empowerments Scale, Effort–Reward Imbalance Questionnaire, Maslach burnout inventory—Human Services Survey and Hospital Anxiety and Depression Scale. Of the 280 healthcare professionals included, 245 had complete data.

Results

Findings confirmed direct predictive paths from the effort–reward ratio to burnout, anxiety and depression. The negative direct path between effort–reward ratio and psychological empowerment was significant, as was burnout and depression prediction by psychological empowerment. Anxiety was not explained by psychological empowerment. Mediation effects were confirmed. Model fit indices indicated a good fit, supporting the role of psychological empowerment in reducing the negative impact of effort–reward imbalance on psychological outcomes, except for anxiety.

Conclusion

Psychological empowerment appears to be a promising way to enhance and protect psychological work‐related health of nursing home healthcare professionals.

Implications for the Profession and/or Patient Care

Understanding the impact of psychological empowerment on healthcare is a first step towards implementing interventions for healthcare professionals to enhance the quality of care and work conditions.

Keywords: anxiety, burnout, depression, healthcare professionals, nurses, nursing home, psychological empowerment, work

Summary.

  • Impact
    • ○
      This study addresses the gap in understanding the role of psychological empowerment on the stress and mental health of healthcare professionals in geriatrics.
    • ○
      Psychological empowerment protects workers from the negative impact of occupational stress on their mental health.
    • ○
      Considering the specific working conditions of nursing homes will allow for further research to develop more targeted interventions for healthcare professionals' mental health.
  • Reporting Method
    • ○
      This study follows the STROBE checklist of EQUATOR's guidelines.
  • Patient or public contribution
    • ○
      No patient or public contribution.
  • What does this paper contribute to the wider global clinical community?
    • ○
      This paper allows for the identification of a protective factor for the organisational and individual issues faced by healthcare professionals.

1. Introduction

The COVID‐19 epidemic highlighted the difficulties of working in geriatric care. Healthcare professionals have direct contact with patients' suffering and disturbances and have to manage issues involving patients, families and colleagues. They also face challenges such as neurodegenerative diseases, complex ethical decisions and patient deaths. Indeed, taking care of people suffering from dementia can be complex and unpredictable and increases the workload, burden and frustration of healthcare professionals (Kang and Hur 2021). Furthermore, last decade, there has been a change in the clinical profile of the residents entering nursing homes. New residents tend to present more severe comorbidities, psychological and behavioural disturbance, and disabilities (Sanchez et al. 2015). Insufficient time for each resident due to organisational and budgetary issues also has a direct impact on the healthcare worker. These shortcomings influence nurses' roles: They are increasingly taking on coordination, management and administrative tasks, requiring them to delegate patient care to nurses' aides (Stewart et al. 2023). These factors place them at a high risk of experiencing psychological distress. Such risk can compromise the quality of care, thereby making nursing home workers' mental health a significant public health concern (Sanchez et al. 2015). Indeed, there is a high prevalence of mental health concerns in this population. Prior to the onset of COVID‐19, studies had already reported a prevalence of burnout syndrome ranging from 25% to 75% among healthcare professionals in geriatric care facilities (Sanchez et al. 2015). Actually, nursing home healthcare professionals have a higher burnout rate (50.1%) than general healthcare professionals (32.2%) (Sanchez et al. 2015). Burnout can be defined as the result of chronic exposition to work‐related issues leading to emotional and physical exhaustion (Canu et al. 2020). According to Maslach (1998), burnout is a multidimensional syndrome composed of emotional exhaustion (i.e., being drained of emotional resources and being emotionally overwhelmed), and depersonalization (i.e., ‘negative, cynical or excessively detached responses to other people’) and poor personal accomplishment (i.e., perception of lacking competencies and productivity at work). However, burnout is not the only concern faced by healthcare professionals. Anxiety (i.e., ‘future‐oriented mood state in which one is ready or prepared to attempt to cope with upcoming negative events’) and depression (i.e., either a persistent depressive mood or a loss of interest or pleasure in most activities) are frequently associated with burnout syndrome (American Psychiatric Association 2013; Barlow 2002). The prevalence of anxiety and depression symptoms ranges from 10.9% to 21.2% for anxiety and 9.8% to 16.9% for depression among this population (Hill et al. 2022). However, the underlying mechanisms contributing to mental health among healthcare professionals in nursing homes remain poorly understood, despite its significant impact, such as increased intent to leave the job and absenteeism (Sanchez et al. 2015).

2. Background

2.1. Effort–Reward Imbalance as an Antecedent of Mental Health

According to Dall'Ora et al. (2020) and Duan‐Porter et al. (2018), diverse job characteristics—high workload, quantitative demands, work culture, low staffing levels, long shifts, low control and working as nurses' aide—are associated with burnout and depression. Job characteristics and mental health's relationship has been demonstrated with the relational aspect of the work (i.e., perceived social impact and perceived social worth) as well (Santos, Chambel, and Castanheira 2020). Seniority within the institution is also a predictor of occupational stress and has a negative impact on several aspects of workers' mental health, such as burnout (Ortega‐Campos et al. 2019). In this context, there is a real need to explore how healthcare professionals handle the challenges of occupational stress, using Siegrist's effort–reward model (Siegrist 1996). Siegrist's model is a combination of the demand–control model (Karasek 1979) and the person–environment fit model (French, Caplan, and Van Harrison 1982). The absence of both reciprocity and equity in the workplace culminates in an effort–reward imbalance (ERI). In Siegrist's model, effort encompasses extrinsic motivation (e.g., informal pressure) and intrinsic motivation (i.e., over‐commitment). Rewards come in three forms: financial referring to salary; status in link with promotions or job security; and socioemotional related to esteem. Effort may take the form of covering for other staff, frequently changing the physical position of residents or managing high levels of responsibility, while rewards result from recognition from superiors, support from staff during overwhelming situations or the relationships they develop with residents. High motivation (i.e., effort) with low reward creates an imbalance. Such an imbalance mainly affects emotions and leads to long‐term stress that, in turn, impacts psychological and physical well‐being (Diekmann et al. 2020; Siegrist 1996). In nursing homes, the risk of developing ERI can be higher than in other healthcare sectors due to high‐stress situations such as behavioural disorders or death of residents, with significant emotional involvement (Sanchez et al. 2015). This raises the question of whether there is balanced reciprocity between effort made and reward received for this population, but the studies investigating this relationship within nursing homes remain very limited.

For other professions, numerous studies have already explored ERI's influence on psychological distress. For anxiety and depression symptoms, studies consistently found a link between high effort/low reward imbalance and psychological distress (Mark and Smith 2011; Zhang et al. 2021). Some studies suggest a direct link between burnout and ERI. In particular, emotional exhaustion seems more influenced by ERI than depersonalization (Aronsson et al. 2017; Leineweber et al. 2021). Rasmussen et al. (2015) have shown that ERI explained 33% of the variance in emotional exhaustion among psychosocial oncologists. However, these relationships have never been tested within the population of nursing home professionals.

2.2. Psychological Empowerment Positive Effects

Although these studies demonstrated that ERI can play a significant role in explaining psychological issues, and working conditions in nursing homes are known to be especially tough, scarce research has focused on what may influence the relationship between ERI and health outcomes. In the field of positive psychology, among the potential protective factors at the individual level, psychological empowerment (PE), developed by Spreitzer (1995), seems to hold a significant place at work. Thomas and Velthouse (1990) defined PE as an enhancement of intrinsic motivation in one's professional setting. The authors identified four cognitions involved in this process: meaning (i.e., the purpose of the job), competence (i.e., self‐evaluation of capacity), self‐determination (i.e., ability to take action) and impact (i.e., perception of influence on the workplace). Some researchers have explored the link between PE and ERI or health outcomes, but not altogether. PE may not be a predictor of ERI (Kluska, Laschinger, and Kerr 2004), and the reversed relationship (i.e., ERI as a predictor of PE) is unclear. Regarding the role of PE on work outcomes, studies have already reported positive correlations (Juyumaya 2022; Meng, Jin, and Guo 2016). In the literature, work engagement, job satisfaction, innovative work behaviour, task performance, job strain and intrinsic motivation are often described as being predicted by PE (Llorente‐Alonso, García‐Ael, and Topa 2023). PE is studied as a mediator that reduces the negative impact of organisational hazards on outcomes in most studies. For example, some research has shown that a higher level of PE among workers is associated with lower levels of burnout and better general mental health (Hochwälder and Brucefors 2005; Meng, Jin, and Guo 2016).

Nevertheless, the literature remains inconsistent about the impact of PE on various psychological outcomes, and it has never been studied in nursing home workers. Methods, such as structural equation modelling (SEM), can provide knowledge on the relationships between such psychological dimensions. Indeed, it allows for the simultaneous estimation of multiple relationships between variables, accounts for measurement error and provides a specific analysis of both direct and indirect effects within a single, unified framework.

3. The Study

3.1. Aim and Hypotheses

The aim of this study is to explore how PE mediates the relationship between ERI and mental health outcomes (burnout, depression and anxiety symptoms) among nursing home staff. Three hypotheses are tested: (1) PE is negatively predicted by ERI; (2) burnout, anxiety and depression are negatively impacted by PE; and (3) ERI is positively predicted by workplace seniority. Therefore, a model (Figure 1) was proposed to test the mediation of ERI on outcomes by PE. Our main hypothesis is that ERI has a direct and indirect effect on health outcomes. In other words, we hypothesised that, as a predictor, PE directly reduces the negative impact of ERI on burnout, depression and anxiety of healthcare professionals.

FIGURE 1.

FIGURE 1

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Theoretical model regarding the mediating role of psychological empowerment on the effects of effort–reward imbalance on burnout, anxiety and depression symptoms.

4. Methods

4.1. Design

This study followed the STROBE checklist for research design. It used a cross‐sectional, exploratory design to investigate the relationship between latent and observable variables using structural equation modelling.

4.2. Study Setting and Sampling

The participants were healthcare professionals recruited from 13 public nursing homes in Southwestern France. We used convenience sampling to select the participants: information regarding the general objectives of the survey was made available within the nursing homes (flyers, posters, emails, etc.), and all professionals were invited to participate. The sample includes different statuses: nurses, nurses' aides, personal care assistants, nurse‐managers, physicians, psychologists, physiotherapists, occupational therapists and psychomotor therapists. To be included in the study, professionals had to be involved in the care of residents, be aged 18 years or older and provide informed consent for study participation. After obtaining approval from institutions directors, a detailed information sheet and participation request were sent to professionals via email. Then, the paper questionnaires were sent to the participants from February 2021 to September 2023. They anonymously returned the questionnaires by depositing them in designated mailboxes located in their workplaces.

4.3. Measures

4.3.1. Demographic Characteristics

Demographic data were requested from respondents, including age, gender marital status, profession, profession seniority and workplace seniority.

4.3.2. Burnout

The Maslach Burnout Inventory—Human Services Survey (MBI‐HSS) is a tool used worldwide to assess burnout among healthcare professionals. The French version consists of 14 items measuring 2 dimensions out of 3 primary Maslach's dimensions: personal accomplishment (PA), emotional exhaustion (EE) and depersonalization (DP). The confirmatory factor analysis (CFA) performed by Lheureux et al. (2017) led to the removal of the PA dimension from the French version. The scale has 7‐point Likert items to investigate the frequency of clinical signs of burnout from 0 ‘never’ to 7 ‘always’. Emotional exhaustion and depersonalization both had a sum score. Higher scores on the MBI‐HSS dimensions indicated higher risk of burnout. The CFA conducted in our sample confirmed the use of two dimensions in the SEM (χ2 (76 N = 245) = 46.84; p = 0.997; CFI = 1.000; TFI = 1.021; RMSEA = 0.040; SRMR = 0.071) (Fit indices cut‐offs are detailed in ‘Data Analysis’ section). Cronbach alphas for emotional exhaustion and depersonalization are, respectively, 0.77 and 0.71 in our study.

4.3.3. Depression and Anxiety

The Hospital Anxiety and Depression Scale (HADS) is commonly used to evaluate these clinical dimensions in the general population (Zigmond and Snaith 1983). It is composed of 14 items, with 7 items assessing anxiety and 7 assessing depression symptoms. Participants answered using a 4‐point Likert scale from ‘0’ to ‘3’ to estimate the frequency of symptoms characteristic of depression or anxiety. Each sub‐scale of the HADS is composed of the sum of its items and has an associated score. A higher score indicates a greater likelihood of anxiety and/or depression symptoms. The French version has been validated in a worker population (Bocéréan and Dupret 2014). CFA has been performed to confirm the use of the depression and anxiety dimensions in the SEM in our study (χ2 (76, N = 245) =131.201; p = 0.000; CFI = 0.921; TFI = 0.905; RMSEA = 0.071; SRMR = 0.080). Cronbach's alphas for anxiety and depression were. 75 and 0.73, respectively. Regarding CFA results, three items from each dimension are used to describe anxiety and depression as latent variables in the measurement model.

4.3.4. Effort–Reward Imbalance

The Effort–Reward Imbalance Questionnaire (ERIQ) is made of three sub‐scales: effort, reward and over‐commitment (Siegrist et al. 2004). Only the 17 items of the effort and reward sub‐scales were used in the study. Participants were required to respond first to a dichotomised ‘agree’ and ‘disagree’ proposition and then 4‐point Likert scales from 1 ‘not disturbed’ to 4 ‘strongly disturbed’ about their perception of work involvement and reward at work. Each item's score of the sub‐scale were calculated, and subsequently, the effort‐to‐reward ratio (ERR) was determined. The ratio was adjusted to account for the variation in the number of items for each dimension. The ratio ranges between 0 and 2. The cut‐off is at 1: When the score is equal or higher than 1, the reward does not justify the effort expended. This condition refers to ERI. Although a newer, shorter version was proposed by Siegrist, its French version has not been validated. Thus, it was chosen to maintain the less recent version, whose psychometric qualities in the French translation are known (Niedhammer et al. 2000). A CFA had been conducted with our sample to confirm the two dimensions in the SEM (χ2 (118, N = 245) = 117.145, p = 0.057, CFI = 1.001, TFI = 1.001, RMSEA = 0.043, SRMR = 0.076). Alphas are between 0.78 and 0.81.

4.3.5. Psychological Empowerment

The PE at work scale (PES) (Spreitzer 1995) consists of four sub‐scales: meaning, competence, self‐determination and impact. The 12 items are used with a 7‐point Likert scale from 0 ‘very strongly disagree’ to 6 ‘very strongly agree’. The subscales' means of each subdimensions were computed to make a composite total score as an indicator of PE. Higher scores indicate a greater level of PE. The structure of the French version has been validated in a CFA (Boudrias et al. 2010). In our sample, a CFA was performed to confirm the sub‐dimensions in the SEM (χ2 (50, N = 245) = 68.68, p = 0.04, CFI = 0.955, TFI = 0.940, RMSEA = 0.039, SRMR = 0.085). For our study population, Cronbach alphas for the four subdimensions were between 0.77 and 0.85.

4.4. Data Analyses

Descriptive analyses report frequencies and percentages for categorical variables and means and standard deviation (SD) for quantitative variables. Even though a selection of items was performed for anxiety and depression in the SEM, the descriptive analyses were conducted using the total scores of the HADS. Levels of burnout, anxiety and depression were categorised as follows: ‘low,’ ‘moderate,’ and ‘high’ levels, according to the original questionnaire thresholds. Correlations were made between quantitative demographic data and our variables of interest. We tested the overall model using the SEM method. Adjustment adequacy of the model was assessed with five criteria: (1) Chi‐square test (χ2), the associated degrees of freedom and p > 0.05, (2) the robust root mean square error of approximation (RMSEA) (< 0.05), (3) the standardised root mean residual (SRMR) (< 0.10), (4) the comparative fit index (CFI) and (5) the Tucker–Lewis Index (TFI) (> 0.95). The cut‐off for all fit indices was defined according to Schumacker and Lomax's (2012) recommendations. χ2/df score also has to be less than 3, as recommended by Bentler and Bonett (1980).

For SEM, estimator of the analysis has to be defined cautiously, considering data characteristics. According to DiStefano and Morgan (2014), a suitable estimator for ordinal variables, non‐normal distributions and small sample is weighted least squares mean and variance adjusted (WLSMV), which was used for this research. The literature is not clear about the minimum sample size required for a SEM. It is commonly established that the greater the sample size, the more fit indices are reliable. Moshagen and Musch (2014) propose to take into account model conditions to conduct a SEM with WLSMV. Indeed, one has to be aware of the impact of the number of indicators per factor, factors loading and the number of categories per scale. In the conditions close to that of our study (i.e., N = 200, 5 categories), more indicators per factor increase the relative percentage bias of the SEM (Moshagen and Musch 2014). Three indicators per factor are shown as the best option to reduce bias in analysis, considering our small sample size. In this context, HADS anxiety and depression are, respectively, measured by the three items with the highest load in the preliminary CFA. MBI‐HSS and PES allow us to use sub‐dimensions of the two concepts as indicators of latent variables. Only the ratio of effort to reward and workplace seniority are used as major observable variables in the model.

Missing data were replaced with the median of the respective variables when the amount of missing data in each questionnaire was less than 10%. This method is known to perform better than mean imputation for skewed distributions (Acuña and Rodriguez 2004). No modifications were made to the missing data of demographic variables.

Descriptive and model analyses were performed on R studio software 4.1.0 using several packages: psych, lavaan, MVN and semTools. (R Core Team (2021). R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R‐project.org/).

4.5. Ethical Considerations

The study is part of the larger project assessing long‐term care facilities in the Landes area of Southwestern France. This research has been approved by an independent research ethics committee (CPP Ouest II Angers—20.11.30.58409). Furthermore, participants had to provide their consent to participate in the study before receiving the paper questionnaire. The instructions were detailed in the questionnaire. The questionnaire return process ensured anonymity.

5. Results

5.1. Characteristics of the Sample

A total of 280 healthcare professionals completed the survey. Of these, 245 participants had fully completed all questionnaires. As shown in Table 1, the majority of the sample was composed of females (91.02%), aged 44 to 56 (33.47%) and reported being married or partnered (71.84%). The three main occupations among the sample were nurses' aides (55.92%), nurses (17.14%) and personal care assistants (14.29%). Concerning their work life, a significant proportion of professionals had been employed in their present institution for less than 1 year (44.49%), 39.59% had less than 10 years of experience, 33.06% had 10–19 years and 25.31% had more than 20 years of experience.

TABLE 1.

Table of the participants (n = 245).

n %
Gender Female 223 91.02
Male 22 8.98
Missing data 0 0.00
Age 18–30 35 14.29
31–43 61 24.90
44–56 82 33.47
> 56 28 11.43
Missing data 39 15.92
Marital status Single 38 15.51
Married/partnered 176 71.84
Divorced/widowed 27 11.02
Declines to make a statement 3 1.22
Missing data 1 0.41
Occupation Nurses 42 17.14
Nurses manager 7 2.86
Nurses' aides 137 55.92
Personal care assistant 35 14.29
Physicians 8 3.27
Others 16 6.53
Missing data 0 0.00
Workplace seniority (years) Less than a year 109 44.49
2–5 75 30.61
5+ 61 24.90
Missing data 0 0.00
Professional experience (years) < 10 97 39.59
10–19 81 33.06
< 20 62 25.31
Missing data 5 2.04
Emotional exhaustion a Low 188 76.73
Moderate 39 15.92
High 18 7.35
Depersonalization b Low 203 82.86
Moderate 30 12.24
High 12 4.90
Anxiety c

Low

149

60.82

Moderate

63

25.71

High

33

13.47
Depression c

Low

213

86.94

Moderate

22

8.98

High

10

4.08
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a

Cut‐offs follow Maslach, Jackson and Leiter's guidelines (1996) for the MBI: low < 17, moderate between 18 and 29, high > 30.

b

Cut‐offs follow Maslach, Jackson and Leiter's guidelines (1996) for the MBI: low < 5, moderate between 6 and 11, high > 12.

c

Cut‐offs follow Zigmond and Snaith guidelines (1983) for the HADS: low (non‐case) < 7, moderate (borderline case) between 8 and 10, high (case) > 11.

As illustrated in Tables 1 and 2, emotional exhaustion predominantly exhibited a low level (76.73%, mean = 12.85, SD = 0.15). Depersonalization and depression were also relatively low within the sample (82.86%, mean = 2.86, SD = 0.15, 4.11, 86.84%, mean = 3.84, SD = 2.99, respectively). In contrast, anxiety levels were higher, with 39.18% exhibiting ‘moderate’ and ‘high’ levels (mean = 7.12, SD = 3.43). Concerning PE, the mean score was 4.78 (SD = 0.74). Effort–reward ratio within the sample indicates a majority of effort–reward adequacy, with a mean of 0.62 (SD = 0.27).

TABLE 2.

Means (M) and standard deviations (SD) of PES's psychological empowerment, ERIQ's effort–reward ratio, MBI‐HSS's burnout dimensions, HAD's anxiety and depression among nursing home workers (n = 245).

Theoretical range Sample range M SD
Effort–reward ratio (ERR) a 0–2 0.32–1.94 0.62 0.27
Psychological empowerment (PES) b , c 0–6 2–6 4.78 0.74
Emotional exhaustion (MBI‐HSS) c 0–54 0–53 12.85 10.15
Depersonalization (MBI‐HSS) c 0–30 0–24 2.86 4.11
Anxiety (HADS) c 0–21 0–18 7.12 3.43
Depression (HADS) c 0–21 0–14 3.84 2.99
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a

Scores above 1 indicate an imbalance in effort regarding rewards.

b

PES is the only scale using an average total score and nota sum of subscales.

c

Higher scores indicate a greater prevalence of the psychological dimension.

Turning into correlations, Table 3 shows correlations between the variables of interest. This table provides evidence that psychological empowerment, burnout sub‐dimensions, depression and anxiety are moderately correlated, which is sufficient to be implemented in the model. Workplace seniority was positively correlated with one subdimension of PE: competence (r = 0.21, p < 0.001). Emotional exhaustion (r = 0.30, p < 0.001), depersonalization (r = 0.25, p < 0.001) and depression (r = 0.32, p < 0.001) were also correlated with workplace seniority, but anxiety was not (r = 0.03, p > 0.05). Conversely, healthcare professionals' age was not correlated with PE, burnout and depression. The number of years in the profession was only correlated with PE competence and depression. Such correlations confirmed the contribution of workplace seniority in the measurement model and the absence of contribution of the number of years in the profession and worker's age.

TABLE 3.

Correlation matrix of outcomes.

1 2 3 4 5 6 7 8 9 10 11
1. Workplace seniority —
2.Age 0.23** —
3.Years in the profession 0.35*** 0.64*** —
4.Meaning (PES). −0.07 −0.09 0.01 —
5.Competences (PES) 0.21*** −0.12 0.20*** 0.39*** —
6.Self‐determination (PES) −0.05 0.09 0.03 0.34*** 0.45*** —
7. Impact (PES) −0.10 0.09 −0.03 0.53*** 0.25*** 0.28*** —
8. Emotional exhaustion (MBI‐HSS) 0.30*** −0.00 0.11 −0.31*** −0.16* −0.29*** −0.35*** —
9.Depersonalization (MBI‐HSS) 0.25*** −0.09 0.05 −0.17** −0.09** −0.24*** −0.16* 0.72*** —
10.Depression (HADS) 0.32** 0.09 0.18* 0.13 0.08 −0.20** 0.01*** −0.09** 0.08 —
11. Anxiety (HADS) 0.03 −0.21** −0.13 0.07 −0.21** −0.07 0.17** 0.02 −0.02 0.52*** —
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Note: Pearson's r correlation. significant: *p < 0.05, **p < 0.01, ***p < 0.001.

5.2. SEM Results

The measurement model was previously confirmed by the CFA performed within each scale (detailed in « methods » section). The structural model comprised four latent variables and 13 observed variables, as described in Figure 2. All fit indices were within stipulated criteria: χ 2 (68, N = 245) = 80.31, p = 0.146, χ 2 /df = 1.18, CFI = 0.96, TFI = 0.95, RMSEA = 0.05, SRMR = 0.07.

FIGURE 2.

FIGURE 2

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Tested model with standardised coefficients.

Our first hypothesis, regarding burnout, anxiety and depression prediction by PE, is partially confirmed, as shown in Table 4. Two paths emerged as significant. Indeed, PE negatively predicted burnout (β = −0.28, p < 0.02), depression (β = −0.32, p < 0.00), but not anxiety (β = −0.10, p = 0.33). Our second hypothesis was confirmed by the significant negative direct path between effort–reward ratio and PE (β = −0.33, p < 0.00). This result confirms our hypothesis, emphasising the fact that PE is predicted by ERR. Then, the mediating role of PE is further shown in Table 5. Indeed, PE had a mediating effect on the ERR–burnout relationship (Z = 0.09, p < 0 0.05). Based on the ratio of the indirect effect to the total effect, 16% of the total effect is mediated by PE. Likewise, PE mediated the ERR–depression relationship, representing 29% of the total effect (Z = 0.11, p < 0.01). Even if the direct effect of ERR on burnout and depression was higher than the indirect effect via PE, the indirect effect remains significant. Consequently, PE has a positive impact on the negative link of ERR on burnout and on depression. As previously seen with coefficients between PE and anxiety (β = −0.10, p = 0.33), the mediating role of PE on the relationship between ERR and anxiety is not significant. These results confirm the mediating role of PE in the relationship between ERR and burnout and depression. Finally, regarding our last hypothesis, ERR is effectively predicted by workplace seniority (β = −0.31, p < 0.001).

TABLE 4.

Results of the structural equation modelling.

Relationship Estimate p value Hypotheses results
ERR—PE −0.33 0.00 Confirmed
PE—burnout −0.28 0.02 Confirmed
PE—depression −0.32 0.00 Confirmed
PE—anxiety −0.09 0.33 Rejected
Workplace seniority—ERR 0.31 0.00 Confirmed
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TABLE 5.

Mediation, direct and indirect effects of effort–reward ratio (ERR) and psychological empowerment (PE) on burnout, depression and anxiety.

Coefficient SE p value
Burnout
Direct effect of ERR 0.68*** 0.11 0.00
Indirect effect of ERR by empowerment 0.09* 0.04 0.02
Total effect 0.77*** 0.10 0.00
Depression
Direct effect of ERR 0.64*** 0.07 0.00
Indirect effect of ERR by empowerment 0.11* 0.03 0.01
Total effect 0.74*** 0.07 0.00
Anxiety
Direct effect of ERR 0.32** 0.07 0.00
Indirect effect of ERR by empowerment 0.03 0.03 0.33
Total effect 0.34*** 0.07 0.00
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6. Discussion

To summarise, the results confirm our model and partially our four hypotheses: (1) burnout and depression are negatively influenced by PE, (2) PE is negatively predicted by ERI, (3) PE mediates the relationship between ERI and psychological outcomes and (4) ERI is positively predicted by workplace seniority.

Regarding the literature, there is a strong and stable empirical evidence for the positive link between job stress and burnout. Our results confirm this, using the Siegrist model, which seems particularly relevant in this context. ERI positively predicts burnout, anxiety and depression symptoms, as previously shown in the literature (Aronsson et al. 2017; Van Der Molen et al. 2020; Zhang et al. 2021). ERI has been shown to explain 33% of burnout among healthcare professionals (Rasmussen et al. 2015). In our study, 46% of burnout and 40% of depression are explained directly by ERI. In France, the age at which older adults enter geriatric care establishments is rising (over 86 years old), so their level of dependence is greater, which increases physical and psychological work overload for nursing staff, with more time spent on hygiene care. Also, due to financial constraints, this work overload has adverse consequences on the quality of the relationship with residents, which is generally a motivating factor (reward) associated with job stability for healthcare professionals.

Moreover, our study confirmed that ERI was positively predicted by workplace seniority (our third hypothesis). Our sample of healthcare professionals may benefit from better mental health and experience less occupational stress compared to the broader nursing staff in geriatrics, possibly due to low workplace seniority, as 45% of our sample have less than one year of workplace seniority. Indeed, the scores of psychological disorders in our study appear to be lower than those reported in recent research. For example, the sub‐scores of burnout syndrome, emotional exhaustion (12.85) and depersonalization (2.86) found in our study are lower than those observed in a study involving French nursing home workers during the COVID period (Conejero et al. 2023) (burnout score: 25.30 for emotional exhaustion score and 7.30 for depersonalization). The prevalence of depression and anxiety in our sample is also lower than that reported in the study by Conejero et al. (2023): 4.08% vs. 10.40% and 13.47% vs. 28.8%, respectively. Results of MBI‐HSS consistently remain lower, aligning more closely with the conclusions of the meta‐analysis by Costello et al. (2019). In our sample, effort–reward ratio was similar to that of previous studies in healthcare professionals. Despite this, ERI was only observed in 9.39% of the total sample. Compared to the results reported by Beschoner et al. (2023), these findings are considerably lower (43.60% lower). A recent work by Țăranu et al. (2022) showed the harmful effects of workplace seniority on healthcare professionals' well‐being. One plausible explanation may be ‘the honeymoon‐hangover effect’. Chadi and Hetschko (2017) highlight the difference in job satisfaction between the first year of work in a new workplace and subsequent periods. As newcomers become more familiar with their workplace, their initial job satisfaction reaches its peak, but over time, this effect diminishes. Therefore, since half of our sample consists of workers with low seniority, we might hypothesise that they have a more positive perception of the workplace, potentially influencing our results concerning their mental health. If workplace seniority partly explains ERI, work environment factors such as the residents/healthcare professionals ratio, quality of care and leadership dynamics could also be explored to protect the well‐being of employees in nursing homes, as it has been already shown in the literature (Stewart et al. 2023). Those factors may offset the negative impact of emotional and physical burden on healthcare professionals regardless of their seniority in the workplace.

Regarding the association between psychological empowerment and psychological distress, our results are consistent with those reported in the literature for depression and burnout. Adding PE increases the variance explained by ERI on burnout to 12% and 15% for depression, confirming the relevance of the model proposed. The protective influence of PE on burnout is broadly recognised, as supported by Şenol Çelik, Sarıköse, and Çelik (2024) systematic review. Positioned as a core psychological resource (Spreitzer 2008), PE is assumed to be a protective factor for occupational mental health (i.e., lack of resource, professional self‐doubt, homework conflict and workload) (Saleh, Eshah, and Rayan 2022). In our study, the level of PE appears to be higher than in other studies among nurses (Harbridge et al. 2023) which could explain the low burnout rate in our sample. Recent longitudinal research by Gillet et al. (2023) supports the idea that workers with a low level of PE are more disposed to depressive symptoms and sleep disorders. Lack of meaning, impact, competence and self‐determination at work can lead to systemic helplessness and have a negative impact on workers' health (Spreitzer, Kizilos, and Nason 1997). As workers with a high level of PEare not totally protected, workers perceiving their job as meaningful may exert more effort and show heightened work engagement, factors that could contribute to reduced well‐being (Gillet et al. 2023). Considering that workers with high or low levels of psychological empowerment may experience poorer mental health highlights that psychological empowerment addresses similar issues to the concept of social reciprocity developed by Siegrist in his theoretical framework (1996). As shown by Peter, Simmonds, and Liaschenko (2016), nurses' moral identity relies on ‘reciprocal fashion, the responses of their patients, including expressions of gratitude’ and ‘maintain[ing] the identity of their patients despite the disruptive forces of illness and hospitalisation’. Psychological empowerment, like social reciprocity, depends on the effort–reward equation. Workers who exert significant effort must be sufficiently rewarded to prevent their significant effort from translating into poorer mental health.

The absence of prediction of anxiety by PE is inconsistent with previous research (Hochwälder and Brucefors 2005; Llorente‐Alonso et al. 2021). Several explanations can be proposed. Those previously cited had investigated anxiety by the mean of broader concepts, in which anxiety is one of several dimensions, such as emotional disorders or general mental health. In these studies, the impact of PE on the prediction of anxiety is not directly explored. Isolating anxiety symptoms may reveal that they are not directly influenced by PE. Two paths can also be explored: low correlations between depression and anxiety items indicates that those variables appear quite independent and the items chosen to measure anxiety might also not fully capture every aspect of distress, impacting the results.

Consistently with our hypothesis, PE was negatively predicted by ERI. Therefore, these results contribute to a better understanding of the link between PE and ERI and confirm the social reciprocity hypothesis. The meta‐analysis by Llorente‐Alonso, García‐Ael, and Topa (2023) has already demonstrated a significant negative correlation between PE and ERI in a general work context. Other research reported a relationship between occupational stress and PE, but these studies used different models of occupational stress than ERI. Quiñónes, Van Den Broeck, and De Witte (2013) reported that PE is influenced by job resources within the Job demands–resources model. Similarly, Spreitzer, Kizilos, and Nason (1997) have also demonstrated that PE can predict job strain in the job strain model. These results are not sufficient to support that PE is predicted by ERI but is not a predictor of it. Future research should explore a bidirectional link between PE and ERI.

Finally, our last hypothesis about the mediation of the relationship between ERI and mental health issues by PE was partially confirmed. Studies have already explored the mediating role of psychological empowerment to limit the negative impact of risk factors on work outcomes such as workplace bullying or abusive supervision (Lyu et al. 2019; Ren and Kim 2023). However, this had not been investigated with ERI. Here, PE mediates the relationship between ERI and psychological outcomes for depression and burnout. With a significant mediation effect in both cases, healthcare professionals with high levels of ERI have low levels of PE, which in turn increases their feelings of burnout and depression. According to the job demands–resources (JD‐R) model (Demerouti et al. 2001), PE is a positive job resource that not only reduces burnout but also promotes personal growth, learning and development.

In our study, healthcare professionals' benefit from a relatively good balance between effort and reward, which increases their PE and consequently reduces psychological disorders. Considering that this specific mediation has not been tested before, our results remain promising.

6.1. Strengths and Limitations of the Work

This study is the first to investigate ERR and PE in a unique model, with the aim of understanding the psychological outcomes of workers in the geriatrics field. Our approach to PE and its relationships appears promising and aligns with existing literature. Additionally, the use of SEM to understand the mediating role of PE within the model is innovative. As previously mentioned, this research is particularly valuable for improving our understanding of the specific situation of healthcare professionals in nursing homes compared to those working in hospital settings.

Our study has several limitations. The sample size remains quite small, even after adjusting the modelling to account for this parameter. Our sampling method also implies some biases: selection bias and low generalisability. In addition, the use of self‐administered questionnaires entails subjective interpretation and answer bias from participants. Also, we did not consider the specificity of each type of healthcare professional in the analysis. As mentioned in the discussion, the items selected for the HADS anxiety and depression measurement model may have impacted our results. Finally, the cross‐sectional exploratory design does not allow inferring causality as the temporal sequence cannot be established.

6.2. Recommendations for Further Research

The main proposal for further research would be to incorporate structural empowerment into the model. Indeed, as highlighted by Şenol Celik, Sarıkose, and Celik (2024) and Meng, Jin, and Guo (2016), structural empowerment is often suggested as a predictor of PE. Structural empowerment (i.e., empowerment provided by the workplace by having access to opportunities, information, support and resources) is closely related to PE, as developed by Kanter (1977). Implementing structural empowerment into this model, as a predictor of ERR and PE, could be very promising. Identifying organisational factors in those nursing homes could also be relevant to understanding the high prevalence of PE observed among healthcare professionals. Concerning the method, suggested changes would be to use a random sampling method. Additionally, using a longitudinal design to investigate causality between variables can provide useful results. As discussed, although PE is generally a protective factor of occupational health, its positive impact can be nuanced (Gillet et al. 2023). It would be useful to better understand the underlying processes and the impact of each dimension to propose relevant interventions in the future aiming to improve PE.

6.3. Implications for Policy and Practice

Introducing positive psychology in occupational health models could be relevant. Depending on the ease of finding a new job, workers are more prone to quit their job if they are in an effort–reward imbalance situation (Țăranu et al. 2022). In professions like healthcare where ERI and turnover intentions are very high, it is crucial to explore ways to minimise the negative impact of ERI from economical, structural and individual perspectives, particularly through PE. In particular, the role of job autonomy is very important. When people perceive their work as autonomous and varied, which is a component of job rewards, it encourages them to take more responsibility, to adopt strategies to overcome a high workload in order to perform well, because they feel they have the opportunity to influence their work environment. These findings have implications for the management and well‐being of healthcare professionals in nursing homes. Addressing structural empowerment at the organisational level, along with empowering leadership and psychological empowerment at the individual level, can play a key role in preserving the mental health of healthcare professionals. This counteracts the adverse effect of ERI on workers' occupational health. The impact of ERI and PE on residents should be considered more broadly, especially in terms of the quality of care (Loerbroks et al. 2016).

7. Conclusion

Our findings support the mediating role of PE between ERI, burnout and depression, with the exception of anxiety. This aligns with the well‐established predictive role of ERI and PE on psychological outcomes. The use of SEM in this research allowed for a more nuanced understanding of these relationships, capturing both direct and indirect effects in a comprehensive global model. Thus, this research enables the consideration of a significant lever to improve healthcare professionals' well‐being, particularly in addressing burnout, in the complex field of geriatrics and nursing homes.

Author Contributions

Amélie Bouche, Nicole Rascle, Hélène Amieva, Camille Ouvrard, Océane Pic made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data. Amélie Bouche, Nicole Rascle, Hélène Amieva, Jacques Jaussaud, Océane Pic, Michèle Koleck, Lucile Dupuy, Camille Ouvrard, Hélène Amieva involved in drafting the manuscript or revising it critically for important intellectual content. Nicole Rascle, Hélène Amieva, Jacques Jaussaud given final approval of the version to be published. Each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content. Amélie Bouche, Nicole Rascle, Hélène Amieva, Océane Pic agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The authors have checked to make sure that our submission conforms as applicable to the Journal's statistical guidelines. There is a statistician on the author team.

Conflicts of Interest

The authors declare no conflicts of interest.

Peer Review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer‐review/10.1111/jan.16709.

Supporting information

Data S1.

JAN-81-5858-s001.docx (31.8KB, docx)

Funding: This study is a component of a project financed by the Roger Spoelberch Fondation, the Caisse Nationale de Solidarité pour l'Autonomie, the Association France Alzheimer, the Observatoire des Mémoires B2V, and the Mutualité Française. Sponsoring entities have no influence on study design, analysis or writing. This research has been approved by an independent research ethics committee (CPP Ouest II Angers—20.11.30.58409–2021/02). The data have been lawfully acquired. Sponsoring entities have no influence on study design, analysis or writing.

Data Availability Statement

Research data are not shared.

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Data S1.

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