Introduction

Sleep quality is a vital component of overall health, characterized by factors such as sleep duration, sleep onset latency, and deep sleep percentage. It is well-documented that poor sleep quality is not only associated with various physical diseases but also with mental health disorders and stress-related syndromes1. This relationship is particularly acute among medical staff, who face unique challenges that compromise their sleep. In China, medical professionals, including doctors, nurses, and technical staff like X-ray operators, often contend with poor sleep quality due to the demanding nature of their roles. Factors such as shift work, long hours, and high occupational stress are prevalent and are exacerbated by a lower ratio of medical staff to the population and higher workload intensities compared to more developed countries2. These conditions are not only a concern for individual health but also pose a significant public health issue. Recent studies, including a meta-analysis, indicate that 39.2% of Chinese medical personnel experience sleep disorders-significantly higher than the general population3. The COVID-19 pandemic has further increased sleep disturbances among healthcare professionals to 45.1%, which is 1.5 times the rate of the general population4. These statistics highlight the urgent need for effective strategies to improve sleep quality among healthcare workers, crucial for their well-being and the overall efficiency of the healthcare system.

Sleep is influenced by an interplay of biological, psychological, and social factors, not only at a specific time but throughout an individual’s development5. Impaired sleep can significantly impact the daytime functionality and efficiency of healthcare workers, potentially compromising the quality of patient care. Biologically, irregular shifts disrupt circadian rhythms, complicating sleep initiation and maintenance. Psychologically, the high-stakes nature of medical work heightens stress and anxiety, which can further degrade sleep quality6. Socially, social factors within the healthcare environment such as the working conditions, work relationships, and an individual’s social support system can either alleviate or exacerbate these issues7. In this context, the Biopsychosocial Model, introduced by George Engel in 19778, provides a comprehensive framework for analyzing the multifaceted sleep challenges faced by healthcare workers. This model integrates biological factors such as circadian rhythm disruptions, psychological elements like stress and anxiety, and social dimensions including the workplace environment and demands9. By adopting this holistic approach, a deeper understanding of the factors affecting sleep quality can be achieved, leading to the development of more effective interventions that consider the complex interactions between biological, psychological, and social influences.

Psychological resilience and sleep quality

Medical work is high-pressure and demanding, often involving life-and-death situations, emotional challenges, and a heavy workload. In such intense circumstances, resilience is crucial for medical professionals to maintain effectiveness and achieve mental and physical balance. Psychological resilience is regarded as an important psychological resource, commonly defined as the ability to bounce back or overcome adversity10. The socio-ecological model of resilience elaborates on this construct, portraying it as a dynamic and multifaceted process through which individuals retrieve or sustain their psychological well-being by tapping into psychological, social, cultural, and physical resources11. Resilience can be categorized into two forms: as an explanatory ability or trait-a consistent personality attribute that aids in overcoming risk elements and preserving developmental functions, adaptability, and mental health; and as a positive psychological outcome or adaptation process12. Previous studies have suggested that both forms of psychological resilience positively influence individuals’ sleep quality. For example, a study by Cai et al. found that resilience was negatively correlated with Pittsburgh Sleep Quality Index (PSQI) scores among disabled elders, indicating that higher levels of resilience significantly improved sleep quality13. Similarly, a survey of postpartum women in Saudi Arabia showed that higher levels of resilience were associated with better sleep quality14. Additionally, a recent longitudinal study revealed that resilience assessed during the first wave predicted sleep quality during the second wave of the COVID-19 pandemic15. Very recently, a cross-sectional survey also found that resilience could predict sleep quality among nurses16.

Resilience theory highlights adaptability and recovery as key to stress response and health maintenance, positioning psychological resilience as crucial for managing work-related stress and sleep quality in medical staff. Grounded in theory and empirical evidence, we propose the following hypothesis:

Hypothesis 1

Psychological resilience significantly predicts sleep quality in medical staff.

The potential mediating role of perceived social support between resilience and sleep quality

Social support is generally conceptualized as the belief in the availability of support from family, friends, and significant others in his/her life. It comprises two dimensions: perceived social support and received social support. Received social support quantifies the objective assistance individuals receive from their social network, while perceived social support pertains to individuals’ subjective perception and assessment of support from family, friends, and significant others17. Research has shown that perceived social support is a stronger predictor of an individual’s mental health compared to received social support18.

Perceived social support is widely recognized as an external protective factor that bolsters psychological resilience, exhibiting a positive correlation with it both in cross-sectional19 and longitudinal20,21 studies, indicating that individuals perceiving greater social support generally exhibit higher levels of resilience. However, it should be noted that while a plethora of studies affirm the positive correlation between perceived social support and resilience, a limited number of previous studies have also indicated a reverse causation-that is, resilience may enhance the perception of social support. For example, Hou et al. found that resilience was positively associated with perceived social support among Chinese nurses22. Chunyu et al. found that patients with substance use disorders who had high levels of resilience also reported high levels of perceived social support23. Resilient individuals are more likely to seek support from their surroundings and tend to expand their social networks, acquiring more support from their established networks24, in contrast with the non-resilient counterparts. On the other hand, scholars have long agreed that perceived social support and sleep quality are strongly related, and that perceived social support significantly predicts an individual’s sleep quality25. Evolutionary psychology proposes that close associations during sleep provide protection from potential dangers, enhancing sleep quality26. Evidence from previous studies has indicated a significantly negative relationship between perceived social support and sleep quality. For instance, Mohamed et al. showed that perceived social support significantly predicted subjective sleep quality in a sample of patients in Somalia27, and Guo et al. reported that adolescents with higher perceived social support exhibited better sleep quality28. Furthermore, medical personnel who perceived higher levels of social support reported better sleep quality during the COVID-19 pandemic29.

In summary, previous findings have highlighted a positive correlation between psychological resilience and perceived social support, as well as a negative association between perceived social support and sleep quality. However, the applicability of these findings to medical staff remains under-explored. Therefore, we hypothesize that:

Hypothesis 2

Perceived social support may mediate the relationship between psychological resilience and sleep quality.

The potential mediating role of psychological distress between resilience and sleep quality

Psychological distress is a well-established and practical concept in mental health, particularly in the development of public health strategies. It is defined as a state of emotional suffering characterized by a combination of depression, anxiety, and stress symptoms that collectively encapsulate intense negative emotional states indicative of maladaptive psychological responses30.

Empirical studies have consistently shown that resilience is negatively correlated with negative indicators of mental health, such as depression, anxiety, stress, and negative affect. Specifically, a meta-analysis revealed a negative correlation between resilience and these negative mental health indicators31. Additionally, extensive research has confirmed the protective role of resilience in the mental health outcomes of medical staff. For instance, a systematic review highlighted resilience as a promotable protective factor against anxiety and work-related stress among physicians32. Our previous research also showed that resilience mitigates the negative impacts, such as depression, resulting from adversity or exposure to traumatic events in Chinese medical staff33.

On the other hand, psychological distress is widely recognized as a factor that can significantly impact sleep quality34. Symptoms of psychological distress, such as anxiety and depression, could lead to considerable suffering and long-term adverse consequences, including poor sleep quality35, including poor sleep quality. A previous cross-sectional study found that Chinese resident physicians who experienced poor sleep quality had a high prevalence of depressive symptoms36. Another study demonstrated that the depression-anxiety-stress state in nurses was positively associated with poor sleep quality37. Furthermore, both cross-sectional and longitudinal studies have established that the relationship between psychological distress and sleep quality is bidirectional, indicating that poor sleep quality can exacerbate symptoms of anxiety and depression, and vice versa38.

The aforementioned studies illustrate a complex interrelationship between resilience, psychological distress, and sleep quality, where each may significantly influence the others. While many studies have investigated psychological resilience as a mediating factor between psychological distress and sleep quality, few have explored the role of psychological distress as a mediator between resilience and sleep quality, especially in medical staff. Therefore, we propose the following hypothesis:

Hypothesis 3

Psychological distress may mediate the relationship between psychological resilience and sleep quality.

The chain mediating role of perceived social support and psychological distress

According to the stress buffering theory, social support serves as a resource to mitigate the negative impact of stress and problems on health, thus maintaining and improving an individual’s mental health outcomes39. Numerous previous studies have indicated an inverse relationship between social support and psychological distress. For instance, Wang et al. found that perceived social support among healthcare workers negatively predicted anxiety symptoms40. Similarly, a systematic review revealed that lower levels of perceived social support were associated with more severe depressive symptoms41. When individuals face psychological distress, social support can serve as a coping mechanism. Therefore, individuals with sleep issues or functional limitations may experience less psychological distress if they perceive adequate social support.

Additionally, prior research has demonstrated that perceived social support may act as a mediator in the relationship between resilience and psychological problems. Hou et al. found that perceived social support partially mediated the relationship between resilience and anxiety in nurses22. A cross-sectional study further confirmed that perceived social support partially mediated the relationship between resilience and burden among caregivers of older adults in Singapore42. Furthermore, several mediation analyses have indicated that higher levels of social support are beneficial in reducing symptoms of anxiety and depression, thus leading to better sleep quality and fewer sleep problems in various population groups, including adolescents and young adults43, stroke patients35, and medical staff29. In summary, the relationship between psychological resilience and sleep quality may be affected first through perceived social support and then via the impact on psychological distress. Thus we finally put forward the following hypothesis:

Hypothesis 4

Psychological resilience may indirectly impact the sleep quality of medical staff by exerting a chain mediating effect on the link between perceived social support and psychological distress.

The current study

Social support and psychological distress significantly influence sleep quality, yet their effects are transient and vary with changing circumstances. In contrast, psychological resilience is a relatively stable trait that not only indicates an individual’s ability to cope with stress but also represents the psychological component of the Biopsychosocial Model, providing consistent protection across various situations44. This study investigates the direct effects of psychological resilience on sleep quality, as well as its indirect influences through perceived social support (a social factor) and psychological distress (a psychological factor) among medical staff.

The relationship between psychological resilience and sleep quality has been explored previously; however, the biopsychosocial mechanisms, particularly among medical staff, require further elucidation. The biological dimension is crucial, as resilience likely influences physiological stress responses and health behaviors, which in turn affect sleep quality. It remains to be determined how resilience impacts healthcare professionals’ perceptions of social support and levels of psychological distress, and whether these factors serve as independent or sequential mediators in the relationship between resilience and sleep quality, ultimately influencing biological processes related to sleep.

Drawing upon extant literature, we have devised a theoretical model, which underpins the following hypotheses grounded in the Biopsychosocial Model: (1) Psychological resilience negatively predicts sleep quality among medical staff, encapsulating the psychological component; (2) Perceived social support acts as a mediator between psychological resilience and sleep quality, illustrating the social component; (3) Psychological distress also serves as a mediator, further emphasizing the psychological component; (4) Psychological resilience exerts an indirect effect on sleep quality through the sequential mediation of perceived social support and psychological distress, elucidating the complex interactions between social, psychological, and biological pathways.

Methods

Ethical statement

The study was approved by the Ethics Committee of Shaoguan University (approval code: yxyllscb202202; approval date: 15 June 2022). All methods adhered to relevant guidelines and regulations. Participants were informed about the study’s purpose and provided voluntary, anonymous consent prior to participation, ensuring privacy protection.

Participants and procedure

This cross-sectional study was conducted in Shaoguan city, Guangdong province, China, between July 5 and 25, 2022. Participants were recruited from two hospitals in Shaoguan using a convenience sampling method. Inclusion criteria required at least one year of hospital work experience and no history of psychiatric illness or family history of psychosis. The survey was conducted on the “Wenjuan Xing” platform, a professional online questionnaire survey network platform (https://www.wjx.cn/app/survey.aspx accessed on 5 July 2022). It was subsequently released on WeChat (known as Weixin in Chinese), a social media tool commonly used by academics to communicate research developments and findings. To ensure the quality of the questionnaire, we excluded questionnaires with an answering time of less than 5 minutes or more than 30 minutes. Additionally, each IP address was limited to one response. Also, we discarded questionnaires with inappropriate response patterns, such as repeatedly reporting of the same response to all questions.

Measures

Demographic variables

The study utilized a self-designed demographic questionnaire to collect participant information, including age, gender, marital status, education level (junior college, undergraduate, and postgraduate), and occupational details such as professional titles (primary, intermediate, and senior), positions (doctors, nurses, and others), monthly income, and years of experience.

Measurement scales

Resilience scale

The Chinese version of the Connor-Davidson Resilience Scale (CD-RISC) was utilized to assess the levels of participants’ psychological resilience. The CD-RISC was originally developed by Connor and Davidson45 and was translated into Chinese by Yu et al. in 200746. The scale consists of 25 items and measures three dimensions: tenacity, strength, and optimism. Participants responded on a 5-point Likert scale ranging from 0 (’never’) to 4 (’always this’). The total scale’s score ranged from 0 to 100 points, with higher scores indicating greater resilience levels. In this study, the Cronbach’s alpha of the CD-RISC was 0.964.

Perceived social support scale

    The Multidimensional Scale of Perceived Social Support (MSPSS) developed by Zimet et al.47 was utilized to to assess the levels of perceived social support. The MSPSS scale consists of 12 items, 3 dimensions: family support, friends support and significant other supports. Participants rated each item rated on a 7-point Likert scale (from 1 = very strongly disagree to 7 = very strongly agree), whose scores ranged from 12 to 84. The mean total score for the MSPSS scale ranged from 1 to 7 points, with higher scores indicating higher perceived social support. The Chinese version of MSPSS translated by Jiang et al.48 has been demonstrated to be a reliable and valid measure among Chinese population. The Cronbach’s alpha of the MSPSS in this study was 0.954.

Depression anxiety stress scale

The Chinese version short-form 21-item Depression Anxiety Stress Scale (DASS-21) was used to measure psychological distress in this sample49. The DASS-21 scale contains three subscales, including depression, anxiety, and stress. Each subscale contains 7 items and there are four options are used to answer each item, ranging from 0 (not applicable) to 3 (mostly applicable). The range of scores spans from 0 to 42 points, whereby scores that are higher in value correspond to more severe levels of distress. The Cronbach’s alpha of the DASS-21 in this study was 0.949, showing excellent internal consistency in the Chinese medical staff.

Pittsburgh sleep quality index scale

    The Pittsburgh Sleep Quality Index (PSQI), developed by D. J. Buysse50 and translated into Chinese by Tsai et al.51, was used to measure sleep quality. The PSQI is a 19-item, self-reported measure of subjective sleep over the past month, containing 7 items that included subjective sleep quality, sleep duration, sleep latency, sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. Items are rated on a 4-point Likert scale from 0 (not during the past month) to 3 (three or more times a week). The seven-component scores are added together to get a global PSQI score, which ranges from 0 to 21, with higher scores representing lower sleep quality. Participants with PSQI scores below 7 points are considered to have good sleep quality, while participants with PSQI scores higher than 7 points are considered to have poor sleep quality52. The Cronbach’s alpha of the PSQI in this study was 0.822.

Statistical analysis

SPSS version 26.0 was used to analyze sociodemographic characteristics, descriptive statistics, and Pearson correlations for key variables: resilience, perceived social support, psychological distress, and sleep quality. Subsequent structural equation modeling (SEM) analysis was performed using Amos 24.0. In this SEM analysis, an initial measurement model was constructed with observed variables representing their respective latent constructs. The hypothesized chain mediating model was then specified by incorporating paths informed by the underlying theoretical framework. The model’s fit was evaluated using various goodness-of-fit indicators: a non-significant chi-square value (\(p> 0.05\)), a root mean square error of approximation (RMSEA) less than 0.05, a comparative fit index (CFI) exceeding 0.95, and a standardized root mean square residual (SRMR) below 0.05 collectively suggest an adequate fit. Upon model validation, path coefficients along with their corresponding p-values were assessed to ascertain the significance of each theorized relationship at p \(< 0.05\). Additionally, both direct and indirect effects of resilience on sleep quality, mediated through perceived social support and psychological distress, were explored. The overall direct and indirect impacts were quantified, and 95% confidence intervals (CIs) were calculated using 10,000 bootstrapping replicated samples. An indirect effect was deemed significant if its 95% CI excluded zero.

Results

Socio-demographic characteristics

After completing the survey, a total of 533 questionnaires were collected, of which 454 were valid, resulting in a valid return rate of 85.17%. Of the 454 participants, 90 (19.8%) were males, 364 (80.2%) were females. Their ages range from 18 to 60 years old, with a mean age of 34.56 (SD=10.24). Moreover, among the participants, 316 (69.6%) were married, 232 (51.1%) had less than 10 years of work experience, and 269 (59.3%) held primary professional titles. Additionally, 244 participants had a junior college education or below, accounting for 53.8% of the total, and 291 medical staff earned less than 6000 China Yuan (CNY) per month, accounting for 64.1% of the total. Among different positions, there were 149 doctors (32.8%) and 234 nurses (51.6%). Furthermore, the mean and standard deviation of the PSQI score were 7.47 and 3.73, respectively, which are much lower than those reported by a previous study among Chinese medical staff during the peak period of the COVID-19 pandemic29. The study revealed that 46% of the participants, or 209 individuals, suffered from poor sleep quality (PSQI \(> 7\)).

Descriptive statistics and correlation analysis

A Pearson correlation analysis was conducted to examine the bivariate correlations among the variables under study. According to the results presented in Table 1, resilience was significantly and positively correlated with perceived social support (\(\gamma = 0.605, p< 0.01\)), while resilience was significantly negatively correlated with psychological distress (\(\gamma = -0.576, p < 0.01\)) and PSQI scores (\(\gamma =-0.46, p < 0.01\)), with higher PSQI scores indicating poorer sleep quality. Moreover, perceived social support was significant negative correlated with psychological distress (\(\gamma = -0.558, p < 0.01\)), as well as PSQI scores (\(\gamma = -0.408, p < 0.01\)). Finally, psychological distress was significant positive correlated with PSQI scores (\(\gamma = 0.531, p < 0.01\)).

Table 1 Correlation analysis of resilience, perceived social support, psychological distress, and sleep quality among medical staff (N = 454).
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Measurement model

In the initial measurement model, there are four latent factors (psychological resilience, perceived social support, psychological distress, and sleep quality) and 16 observed variables. Since the factor loadings for both habitual sleep efficiency and the use of sleeping medication on the latent variable of sleep quality were too low (\(<0.4\)), they were ruled out from the model. After modification, the test of the revised measurement model resulted in an acceptable fit to the data, indicated by the following goodness of fit statistics: \(\chi ^2 = 156.035, df = 72, {\chi ^2}/{df} = 2.167, p <0.001; SRMR = 0.036; RMSEA = 0.051; NFI = 0.956; CFI = 0.977; GFI = 0.948; AGFI = 0.927\). Moreover, standardized factor loadings in the revised model ranged from 0.48 to 0.92 (see Fig. 1), showing that they loaded significantly in the predicted directions in their respective constructs (all \(p <0.05\)).

Structural model

To find a better relationship among the study variables, we tested three alternated models. Specifically, a full mediated model (Model I), which contained mediators (perceived social support and psychological distress) and no direct link from resilience to sleep quality, was firstly established and assessed. The results showed that Model I fitted the data well \([~ \chi ^2 = 247.964, df = 99, {\chi ^2}/{df} = 2.505, \textit{p} <0.001; CFI = 0.966; GFI = 0.936; IFI = 0.966; SRMR = 0.0569; RMSEA = 0.058 ]\).Then, a partially mediated model (Model II) that drew a direct path from resilience to sleep quality was tested. While the fit indices of Model II were acceptable \([ \chi ^2 = 236.486, df = 98, {\chi ^2}/{df} = 2.413, p <0.001; CFI = 0.968; GFI = 0.94; IFI = 0.969; SRMR = 0.0551; RMSEA = 0.056. ]\), the path between PSS and sleep quality was not significant ( \(\beta = 0.003, {p} = 0.963>0.05\)). Finnaly, we removed the direct pathway from PSS to Sleep Quality and add the path from PSS to psychological distress (Model III). Model III indicated a good model \([ \chi ^2 = 199.968, df = 98, {\chi ^2}/{df} = 2.04, \textit{p} <0.001; CFI = 0.977; GFI = 0.948; IFI = 0.977; SRMR = 0.0413; RMSEA = 0.048 ]\). In addition, all path coefficients were found to be significant. Moreover, it was seen that the AIC and ECVI values in Model III (AIC=222.035; ECVI=0.49) were lower than those in Model I and Model II. Therefore, Model III was preferred (see Fig. 1) because of the significant pathways, better-fit indices, and lower AIC and ECVI values, as shown in Table 2.

Figure 1
figure 1

The chain-mediation model of psychological resilience, perceived social support,  psychological distress, and sleep quality. It includes values for standardized regression coefficients for direct paths. ***p \(< 0.001\).

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Bootstapping

The study employed the bootstrapping procedure of AMOS 24.0 to test the significance of the partially chain mediated model, i.e., Model III, with a bootstrap sample of 10,000 was specified. Moreover, control variables, including age, work years, professional title, monthly incomes, and positions, were set to reduce statistical errors52.

Direct, indirect, and total effects are presented in Table 3 and Fig. 1. It was seen that the link from psychological resilience to PSQI scores was significant (\(\beta = - 0.26\), p \(< 0.001\)). Furthermore, psychological resilience was found to have a significant positive correlation with perceived social support (\(\beta = 0.669, p < 0.001\)) and a significant negative correlation with psychological distress (\(\beta = -0.433, p < 0.001\)). In addition, PSS was found to have a significant negatively impact on medical staff’s psychological distress (\(\beta = -0.344\), \(p < 0.001\)). Finally, it was found that psychological distress was positively associated with PSQI scores (\(\beta = 0.489\), \(p < 0.001\)).

As shown in Table 3, the indirect effect of psychological resilience on sleep quality via psychological distress was statistically significant (boot strap estimate = \(-0.027\), 95% CI = [\(-.037, -.019\) ]), indicating that psychological distress significantly mediated the relationship between resilience and sleep quality. Therefore, hypothesis 3 was confirmed. Moreover, the indirect effect of psychological resilience on sleep quality, via perceived social support and psychological distress serially (boot strap estimate = \(-0.014\), 95% CI = [ \(-.023, -.008\) ]), suggested that perceived social support and psychological distress played a chain mediating role in the relationship between psychological resilience and sleep quality. Therefore, hypothesis 4 was supported, confirming the establishment of chain mediation.

Table 2 Model fit indices for structural models.
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Discussion

The main goal of this research was to explore how resilience and sleep quality are linked in Chinese medical staff. We focused on how the social support they feel and their psychological issues (such as depression, anxiety, and stress) might play a role in this connection. Guided by the biopsychosocial model, we constructed a chain mediation model to dissect the indirect effects of perceived social support and psychological distress on the aforementioned relationship. The results of this study demonstrated that psychological resilience not only directly impacts the sleep quality of medical staff but also exerts an indirect influence via the sequential mediating effects of perceived social support and psychological distress. Our study is the first to examine perceived social support and psychological issues as possible reasons for the link between resilience and sleep quality among medical staff.

Firstly, the results in this study showed that psychological resilience was negatively associated with PSQI scores, indicating that higher levels of psychological resilience could significantly improve the quality of sleep among Chinese medical staff. This finding supported hypothesis 1 and was consistent with previous research on medical workers16,53, which showed that psychological resilience played a protective role in sleep quality. According to the neuroscience theory of resilience54, psychological resilience is linked to brain regional structure, neural circuits, and brain neural networks, and brain function. These factors play a crucial role in emotional regulation, which indirectly affects sleep quality7. Furthermore, possessing high levels of psychological resilience could assist medical professionals in confronting occupational stressors and setbacks in a positive manner, effectively coping with adversity, reducing the negative impact of work-related high-stress events, and thus promoting the development of physical and mental health55,56. Therefore, psychological resilience may improve the quality of sleep among medical professionals by positively impacting their physical and mental health.

Secondly, the study found that perceived social support did not play a significant role in mediating the relationship between psychological resilience and sleep quality among medical staff. Therefore, hypothesis 2 was not supported. Specifically, this study indicated that perceived social support was not a significant predictor of sleep quality in the constructed mediation model. The results revealed that even with high levels of perceived social support, resilient medical staff might still suffer from poor sleep quality. In fact, inconsistencies exist in the literature regarding whether perceived social support could predict sleep quality57. While the majority of previous studies showed that (perceived) social support was positively related to sleep quality25,27,28, a minority of studies indicated that there was no significant association between social support and sleep quality in different populations29,57,58. Notably, our study of Chinese healthcare professionals during the COVID-19 pandemic offers a distinct context that could help explain these disparities.The intense workload and stress faced by healthcare professionals, especially during the pandemic, are unprecedented. They often endures lengthy shifts under immense strain, which might negate any direct beneficial effects of social support on sleep quality. Despite robust social support, their substantial work demands and stress could impede the conversion of social support into better sleep.In such a demanding environment, the influence of social support may be overshadowed by immediate job requirements, diminishing its direct effect on sleep outcomes. Moreover, these discrepancies could be due to variations in samples, cultural backgrounds, and measurement tools. Therefore, further studies are strongly recommended.

Thirdly, in line with Hypothesis 3, this study found that psychological distress played a significant mediating role in the relationship between resilience and sleep quality among the medical staff. Specifically, psychological resilience was found to negatively predict psychological distress, which, in turn, positively predicted sleep quality. On one hand, the study found that higher levels of resilience in medical staff were associated with lower levels of psychological distress. This finding is partially consistent with previous research which has shown that resilience can act as a buffer against stress and adversity. For instance, a recent cross-sectional study involving 602 medical staff revealed that high levels of resilience contributed to lower levels of depression59. Furthermore, resilience has been identified as a promotable protective factor against anxiety and work-related stress in nursing staff32. Psychological researchers generally acknowledge that individuals with higher levels of resilience have access to more psychological resources, such as fortitude, optimism, and emotion regulation abilities60. Within this framework, resilient healthcare workers are more likely to possess stronger anti-stress capabilities and better social adaptability, enabling them to actively and positively deal with setbacks or diversity in life34. Such attributes not only reduce the impact of negative stress events on their well-being but also further promotes the development of their physical and mental health. Consequently, medical staff with higher levels of psychological resilience may have better mental health, whereas those with lower levels of psychological resilience may be more susceptible to emotional dysregulation following exposure to stressors or other negative emotions61. On the other hand, there was a significant positive correlation between psychological distress and lower sleep quality. That is, higher levels of psychological distress were associated with poorer sleep quality. The results are in line with previous studies62,63, which also found a strong association between more severe depressive symptoms and poor sleep quality in healthcare workers. One potential explanation is that depressive symptoms, such as diminished energy, inattention, and daytime drowsiness, could disrupt circadian rhythms and impair sleep quality63. In summary, our research demonstrates that psychological resilience can enhance sleep quality by diminishing psychological distress in healthcare workers, thereby contributing to the expanding body of knowledge on the emotional mechanisms underlying psychological resilience and sleep quality.

Finally, the study contributed to providing evidence for the chain mediating effect of perceived social support and psychological distress on the relationship between psychological resilience and sleep quality among healthcare professionals. On the one hand, the current study found that the psychological resilience could significantly predict perceived social support in medical staff. Although it has been well documented that a high level of perceived social support correlates with increased resilience64,65,66, this study indicates that resilience, in turn, can also predict perceived social support in the medical staff group, corroborating findings from studies involving other populations24,42. The rationale behind this may involve several factors. Medical staff with greater resilience are prone to express their thoughts and find sympathetic friends, which are salient factors for broadening their social networks22. This enables them to acquire and perceived sufficient support from people around them to cope with various stressors and obstacles encountered in their daily professional lives29. Furthermore, resilient medical staff tend to maintain a more positive outlook on their current circumstances and exhibit a more proactive approach towards seeking professional assistance23. Consequently, resilient medical staff might report higher levels of perceived social support compared to their less resilient counterparts, even if they have the same actual social support. These findings contribute new insights into the emotional mechanisms underlying the positive effect of resilience on perceived social support, particularly among medical staff group. On the other hand, this study found that perceived social support was significantly negatively associated with psychological distress among medical personnel, aligning with previous studies33,67. A compelling rationale for this result is that high levels of perceived social support can provide emotional comfort and warmth24, as well as bolster self-assurance in tackling adverse circumstances and stressful encounters, thereby mitigating the negative emotional experiences of medical staff29,63. Moreover, this finding also echoed the main effect model of social support, which argued that social support could beneficially influence mental health by alleviating anxiety and depression68. Therefore, perceived social support emerges as a pivotal element in shielding medical professionals from the deleterious effects of psychological distress.

In conclusion, while the direct mediating role of perceived social support between resilience and sleep quality in medical professionals was not significant, our analysis uncovered a more intricate relationship. We confirmed through our data an empirical validation of hypothesis 4, which proposed a three-path mediation mechanism: resilience enhances perceived social support, subsequently reducing psychological distress and consequently leading to improved sleep quality. Defining these pathways deepens our comprehension of how resilience engages with social support and emotions to influence sleep-related biological outcomes. This refined analysis is consistent with the Biopsychosocial Model, advancing a comprehensive grasp of the determinants of sleep quality and guiding the design of tailored interventions to address the diverse facets of well-being in healthcare professionals.

Table 3 Chain mediating effect of perceive social support and psychological distress on the relationship between psychological resilience and sleep quality in medical staff. Bootstrap sample size = 10,000. PR Psychology Resilience, PSS Perceived social support, PD Psychological Distress, SQ Sleep Quality.
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Limitations and implications

This study, while providing valuable insights, has several limitations. Firstly, the cross-sectional design restricts the ability to ascertain causal relationships among resilience, perceived social support, negative emotions, and sleep quality. Future research should employ experimental or longitudinal methodologies to establish causality. Secondly, the potential response bias and social desirability bias in online self-reported questionnaires cannot be completely eliminated. Future studies could benefit from incorporating objective measurements to mitigate these biases. Additionally, the generalizability of our findings may be constrained as the sample of medical staff was drawn from only one city in China using a convenience sampling method. It is important to consider that the cultural and operational environments of medical staff in different regions or countries may vary significantly. These differences may influence the relationships among resilience, perceived social support, negative emotions, and sleep quality. Therefore, caution should be exercised when generalizing these results to other populations and cultural contexts. Future studies are encouraged to replicate this study in diverse geographical areas and cultural settings to enhance the external validity of these findings. Finally, while this study explored the mediating roles of perceived social support and negative emotions, other potential mediators such as dimensions of professional quality of life (e.g., compassion satisfaction, burnout, secondary traumatic stress) and mindfulness should be considered in future studies.

From a practical standpoint, the study’s findings provide critical guidance for developing evidence-based interventions aimed at enhancing resilience and sleep quality among medical staff. To this end, several targeted interventions can be considered. Firstly, workshops and educational sessions on sleep hygiene can educate medical staff on adopting healthy sleep habits. These sessions could cover topics such as the importance of a consistent sleep schedule, creating a restful sleep environment, and managing pre-sleep anxiety. Secondly, evidence-based stress management programs, such as cognitive-behavioral therapy (CBT) and mindfulness-based stress reduction (MBSR), have been demonstrated to effectively help medical staff manage stress and improve sleep quality69. These programs should be integrated into staff training and development. Thirdly, programs designed to enhance resilience should include elements such as problem-solving training and emotional regulation skills. These interventions can assist medical staff in developing more effective coping mechanisms for workplace stressors, which may in turn improve sleep quality32. Additionally, fostering strong social support networks and positive relationships among colleagues is vital. Medical institutions should facilitate a supportive culture through team-building activities and create an environment where seeking help is encouraged and valued. Furthermore, organizational interventions should focus on creating a work environment that promotes open communication, provides adequate breaks, and manages workloads effectively to reduce stress levels. This includes ensuring that counseling services and psychotherapy are readily accessible to staff, helping them to address psychological distress and manage stress.

Conclusion

The current study revealed that higher levels of resilience were associated with enhanced sleep quality, as well as improved perceived social support and decreased negative emotional experiences among medical staff. Furthermore, the relationship between resilience and sleep quality was found to be partially mediated by psychological distress. Additionally, perceived social support and psychological distress were found to play a chain mediating role in the relationship between resilience and sleep quality. These insights suggest that comprehensive improvements in sleep quality among medical personnel require not only fostering greater resilience but also improving perceived social support and alleviating psychological distress.