Content area
Objectives
To investigate how post-traumatic growth (PTG) and moral sensitivity influence service behaviour among healthcare workers (HCWs) in mainland China post-COVID-19, with a focus on the mediating role of moral sensitivity.
Design
Cross- sectional survey design.
Setting
This study was conducted in 27 provinces across mainland China, from 16 March to 2 April 2023.
Participants
1,193 HCWs, including 378 physicians and 815 nurses, were selected using convenience and snowball sampling methods.
Methods
The survey included the Post-traumatic Growth Inventory-Chinese version (PTGI-C), the Moral Sensitivity Questionnaire-Revised Chinese Version (MSQ-R-CV) and a service behaviour scale. Structural equation modelling was employed to analyse the data, focusing on the associations between PTG, moral sensitivity, and service behaviours.
Results
The study found significant associations between PTG and moral sensitivity (r=0.49, p<0.01), with both factors positively influencing HCWs’ service behaviours. Specifically, PTG had a direct effect on service behaviours (β=0.172, p<0.01) and an indirect effect through moral sensitivity (β=0.333, p<0.01), with moral sensitivity mediating 65.8% of PTG’s impact on service behaviours. The model explained 56.0% of the variance in service behaviours, indicating a substantial influence of these psychological factors on professional conduct.
Conclusions
The findings highlight the significant role of PTG and moral sensitivity in shaping the service behaviours of HCWs in the aftermath of the COVID-19 pandemic. The study suggests that enhancing PTG and moral sensitivity through targeted interventions could improve HCWs’ service delivery and resilience, emphasising the importance of incorporating psychological and ethical training into healthcare practices to prepare for future public health crises.
Full text
Correspondence to Dr Dianjiang Li; [email protected]
STRENGTHS AND LIMITATIONS OF THIS STUDY
Employed structural equation modelling to analyse complex relationships between post-traumatic growth, moral sensitivity and service behaviour, providing robust statistical insights.
Used validated assessment tools, enhancing the reliability of the findings.
The cross-sectional design limits the ability to establish causal relationships among the variables.
Recruitment through convenience and snowball sampling may compromise sample representativeness and the generalisability of the results.
Reliance on self-reported measures could introduce response biases, such as social desirability bias, leading to overreported levels of moral sensitivity and service behaviour.
Introduction
The COVID-19 pandemic, a prolonged and intense public health crisis, has significantly impacted behaviours in various sectors. Healthcare workers (HCWs) in China, including physicians and nurses, have been crucial in addressing COVID-19 for nearly 3 years. They have endured substantial mental stress due to high workloads, a large number of critically ill patients, patient fatalities, infection risks and rapid shifts in care demands.1 2 The cessation of China’s zero-COVID-19 policy on 7 December 2022 marked a major transition in the country’s response to the pandemic, characterised by ending strategies like close contact tracing, mass nucleic acid testing and centralised quarantine.3
Concurrently, a nationwide outbreak of COVID-19, predominantly involving Omicron infections, occurred from November 2022 to February 2023.4 This outbreak severely strained healthcare resources, leading to a critical shortage of HCWs and essential supplies. Despite being infected themselves, many HCWs continued to work, facing even greater stress and psychosomatic issues than during previous outbreaks.5 In addition to these challenges, HCWs in China dealt with unique obstacles, including prolonged exposure to high-risk environments and significant alterations in their work routines due to policy changes. These conditions, starkly different from those in global contexts owing to the varying lengths and severities of lockdowns and healthcare strategies, underscore the urgent need to investigate service behaviour in this specific context to enhance preparedness for future global health crises.
The psychosomatic well-being of HCWs not only impacts their personal lives but also their service behaviours, which are critical for hospital operations. High-quality nursing care is linked to greater patient satisfaction and positive reviews, enhancing professional performance and work efficiency.6 As Zhang et al 7 note, HCWs’ service behaviour divides into in-role and extra-role behaviours. In-role service behaviour (ISB) comprises mandated actions set based on workplace norms or job factors, such as key performance indicators. Extra-role service behaviour (ESB), in contrast, includes informal actions that go beyond formal job responsibilities.
The interplay between mental health and service behaviour is particularly relevant, as prior research has explored the service behaviours of nurses and physicians across various specialties and pandemic stages, with a focus on influencing factors.7–9 However, the impact of COVID-19-induced psychological and behavioural shifts on HCWs’ service behaviour, especially in the postpandemic era, has been insufficiently studied. This era, characterised by increased job complexity, greater responsibilities, higher expectations and limited authority for HCWs, offers new challenges and opportunities for enhancing HCW service behaviour in an evolving healthcare environment.
Service behaviour and post-traumatic growth
HCWs’ service behaviour is closely linked to their mental health, directly affecting the quality of care10 and the incidence of medical errors.11 Recent studies have highlighted COVID-19’s significant psychological impact on HCWs, presenting both negative and positive changes. The negative changes include anxiety, depression, sleep disorders, post-traumatic stress and acute stress.12 Conversely, positive changes, known as post-traumatic growth (PTG), arise from effectively managing stressful situations.13 PTG leads to an enhanced appreciation of life, the discovery of new possibilities and strengthened relationships. Additionally, overcoming adversity can result in increased interest in spiritual matters and inner strength.14 While much of the literature focuses on the negative changes affecting HCWs’ care and service behaviour, the potential benefits of positive change are less emphasised. This disparity points to the need for further exploration of how positive psychological adaptations can positively influence HCWs’ professional performance.
Service behaviour and moral sensitivity
Studies indicate that HCWs encountered significant ethical dilemmas due to multifaceted challenges during the pandemic.15 These challenges encompassed professional responsibilities, social pressures and continuous exposure to infection, serious illness or death. Central to navigating these dilemmas is moral sensitivity, which enables HCWs to identify and address ethical issues effectively.16 This skill is crucial for making compassionate and intelligent decisions, taking into account patients’ physical, spiritual and emotional needs along with the inherent uncertainties in medical care.17 Enhanced moral sensitivity particularly benefits nurses, providing them with advanced skills to resolve value conflicts, thereby improving the quality of medical services.18
The mediating role of moral sensitivity
There are strong links among HCWs’ psychological changes, moral sensitivity and service behaviour. Both positive and negative psychological shifts can significantly impact moral sensitivity. Taylan et al 16 observed that nurses dealing with conflict and distress tend to have increased sensitivity to ethical dilemmas. Supporting this, Fang et al 19 found that ethical dilemmas often first evoke strong emotions, a psychological reaction, before being processed through complex cognition. These emotional reactions are crucial for stimulating moral cognition, which then influences an individual’s moral sensitivity.20 Furthermore, research has shown that clinical nurses with elevated levels of moral sensitivity are better equipped to deliver high-quality service.15 21 22 This underscores the integral role of moral sensitivity in connecting psychological changes with service behaviour.
Conceptual framework and hypotheses
This study is based on the stimulus-organism-response (SOR) framework by Mehrabian et al,23 originally from environmental psychology but effectively adapted for healthcare settings. The SOR model helps examine how external stimuli—such as technological innovations and psychological factors—affect emotional and cognitive states, which then influence behaviours. For instance, Yang et al 24 used it to explore patient adherence to online pharmacy services, showing the influence of service characteristics on adherence. Chudhery et al 25 studied physicians’ intentions to prescribe via mobile healthcare systems, finding that system attributes impact trust and satisfaction. Li et al 26 investigated physicians’ switching to Internet of Things–based rehabilitation systems, emphasising quality attributes’ effects on perceived usefulness and behavioural responses. These studies highlight the SOR model’s applicability and predictive power in healthcare.
Within the COVID-19 pandemic context, various studies have applied the SOR model to explore adaptive behaviours and psychological impacts. For instance, Mladenovic et al 27 used it to analyse how individuals sought and shared COVID-19 information, while Pandita et al 28 assessed its psychological effects on students, demonstrating the model’s ability to address both informational and emotional responses. Building on these applications, our study examines the complex interplay between psychological changes, moral sensitivity and service behaviour among HCWs in the post-pandemic era. We particularly focus on PTG as the stimulus—a positive psychological change spurred by the challenges of the pandemic that reshapes both personal and professional perspectives and catalyses emotional and cognitive transformations in healthcare professionals. Moral sensitivity is conceptualised as the organism in our framework. It serves as the mediating internal state that interprets the PTG stimulus, influencing decision-making in clinical settings. This enhanced moral awareness is pivotal in navigating the ethical complexities HCWs face, especially after a global health crisis. The response in our model is the observable service behaviour of HCWs, which includes both mandated and discretionary actions crucial for maintaining patient care quality and operational efficiency. By understanding this relationship, our study aims to illuminate how PTG, mediated by moral sensitivity, can potentially enhance service behaviours in the post-pandemic era. To systematically explore these dynamics, we have formulated four hypotheses：
Hypothesis 1. PTG directly influences HCWs service behaviour.
Hypothesis 2. PTG directly influences HCWs’ moral sensitivity.
Hypothesis 3. Moral sensitivity directly influences HCWs service behaviour.
Hypothesis 4. PTG indirectly influences HCWs’ service behaviour through moral sensitivity.
The hypothesised model is depicted in figure 1.
Methods
Design and sample
This research employed a cross-sectional survey design, integrating convenience and snowball sampling methods. It used Wenjuanxing (www.wjx.cn), an online platform for creating, distributing and temporarily storing questionnaires. The questionnaire link was distributed via WeChat, China’s popular social media platform. Initially, the researchers shared the link with known HCWs, including physicians and nurses, who then further disseminated it among their peers in various hospitals. Participation required explicit consent through an affirmative click, with automatic logout for non-consent. Each participant could use their ID only once, and all survey questions were mandatory to complete before submission to ensure data integrity. The survey was conducted from 16 March to 2 April 2023, receiving responses from 1289 HCWs across 27 provinces in mainland China. Responses completed in less than 90 s were excluded to maintain quality, resulting in 1193 valid questionnaires and a 92.55% response rate.
Instruments
To achieve the study objectives, validated questionnaires available in Chinese were selected.
Post-traumatic growth
PTG was assessed using the Chinese version of the Post-traumatic Growth Inventory (PTGI-C), adapted by Wang et al 29 from the original PTGI created by Tedeschi and Calhoun.13 Designed for compatibility with Chinese culture, the PTGI-C comprises 20 items distributed over five subscales, following the removal of one item (item 18) during adaptation. These subscales are designed to assess various aspects of PTG: (1) relating to others (three items), which measures increased closeness with others, such as ‘I have a greater sense of closeness with others’; (2) new possibilities (four items), which explores the emergence of new life paths, for example, ‘I developed new interests’; (3) personal strength (three items), which assesses self-perceived resilience, like ‘I have a greater feeling of self-reliance’; (4) spiritual change (four items), which evaluates a deeper understanding of spiritual matters, exemplified by ‘I have a greater appreciation for the value of my own life’; and (5) appreciation of life (six items), which reflects an increased appreciation for daily life, such as ‘I have a stronger religious faith’. Responses were recorded on a 6-point Likert scale ranging from 0 (‘I did not experience this change due to the COVID-19 pandemic’) to 5 (‘I experienced this change to a very great degree due to the COVID-19 pandemic’), with higher scores indicating more significant PTG. The reliability of the PTGI-C in this study was confirmed by Cronbach’s alpha coefficients, which was 0.971 for the overall score and ranged from 0.835 to 0.926 for the subscales: relating to others (α=0.885), new possibilities (α=0.919), personal strength (α=0.888), spiritual change (α=0.835) and appreciation of life (α=0.926).
Moral sensitivity
Moral sensitivity was assessed using the Chinese-translated Moral Sensitivity Questionnaire-Revised Version (MSQ-R-CV), adapted by Huang et al 30 from Lützén et al’s Moral Sensitivity Questionnaire (MSQ).31 The MSQ-R-CV includes nine items, categorising moral sensitivity into two key domains: (1) moral burden, featuring four items that assess an individual’s preparedness to confront situations conflicting with moral values, exemplified by statements like, ‘When caring for patients, I am always aware of the balance between the potential for doing good and the risk of causing harm’, and (2) moral strength and responsibility, which includes five items that measure the ability to recognise the moral implications of one’s actions and uphold commitments under moral rules and values, such as ‘I always feel a responsibility to ensure the patient receives adequate care, even when resources are scarce’. Responses are measured on a 6-point Likert scale, from total disagreement (1) to total agreement (6), allowing scores to range from 9 to 54. Higher scores denote increased levels of moral sensitivity. In this study, the Cronbach’s alpha coefficients for the MSQ-R-CV was 0.907 for the overall score, 0.701 for the moral burden subscale and 0.882 for the moral strength and responsibility subscale.
Service behaviour
HCWs’ service behaviours were evaluated using an eight-item scale developed by Chen,32 comprising two categories: ISBs (five items), exemplified by ‘In the last month, I have adhered to hospital rules and regulations in providing medical or nursing services,’ or ESBs (three items), such as ‘In the last month, I volunteered to provide additional services when needed by patients’. Responses were collected using a 5-point Likert scale, ranging from total disagreement (1) to total agreement (5), where higher scores indicate more pronounced service behaviours. The reliability of the scale was confirmed in this study, with a Cronbach’s alpha coefficient of 0.960 for the overall scale and 0.953 and 0.941 for the ISB and ESB subscales, respectively.
Sociodemographics
Sociodemographic information included gender, age, occupation, educational status, marital status and COVID-19 infection status. Information related to work status included working years and professional title. The geographic regions, namely northern and southern China, were delineated by the Yangtze River.
Statistical analysis
Categorical variables are represented as frequencies (percentages) and continuous variables as the mean±SD. Pearson correlation analysis was employed to evaluate bivariate relationships among variables. Descriptive statistical analyses were executed using SPSS (V.22; IBM Corp.). Structural equation modelling (SEM) was conducted in AMOS (V.24; IBM Corp.) following Anderson and Gerbing’s two-stage approach,33 initially testing the measurement model followed by the hypothesised structural model. Due to the presence of multivariate non-normality, we opted for maximum likelihood estimation coupled with percentile bootstrapping—a technique well-suited for non-normal distributions.34 To assess the reliability and validity of the measures, we adopted Hair et al’s methodology,35 which involves evaluating factor loadings, composite reliability (CR), average variance extracted (AVE) and Cronbach’s alpha (α). Acceptable thresholds were set at factor loadings ≥0.5, CR ≥0.7, AVE ≥0.5 and α ≥0.7. Model fit was evaluated based on several criteria: a χ 2/df (χ 2/df) ratio less than 5.00, goodness of fit (GFI) and related fit index (RFI) values of 0.90 or greater, and a root mean square error of approximation (RMSEA) less than 0.08.36 Fit statistics were further adjusted using the Bollen-Stine bootstrapping procedure.37 A two-tailed P value <0.05 denoted statistical significance.
Patient and public involvement
Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.
Results
Description of participants
The participants’ sociodemographic information is shown in table 1. The mean age of the participants was 33.12 (SD=9.25) years, with individuals aged over 40 years constituting 20.6% of the cohort. The majority held a bachelor’s degree (58.6%), were female (77.3%) and worked as nurses (68.3%). Among them, 63.9% were married, 87.3% had experienced COVID-19 infection and 63.3% resided in Southern China.
Table 1Sociodemographic characteristics of surveyed healthcare workers (n=1193）
| Characteristic | Category | Frequency | % |
| Gender | Female | 922 | 77.3 |
| Male | 271 | 22.7 | |
| Age (years) | 18–30 | 528 | 44.3 |
| 31–40 | 419 | 35.1 | |
| >40 | 246 | 20.6 | |
| Occupation | Physician | 378 | 31.7 |
| Nurse | 815 | 68.3 | |
| Education | Certificate (technical school) | 53 | 4.4 |
| Junior college | 277 | 23.2 | |
| Bachelor’s degree | 699 | 58.6 | |
| Master’s degree or above | 164 | 13.7 | |
| Marital status | Unmarried* | 431 | 36.1 |
| Married | 762 | 63.9 | |
| COVID-19 infection status | Yes | 1042 | 87.3 |
| No | 124 | 10.4 | |
| Not sure | 27 | 2.3 | |
| Working years | ≤5 | 505 | 42.3 |
| 5–10 | 226 | 18.9 | |
| 10–20 | 319 | 26.7 | |
| >20 | 143 | 12.0 | |
| Professional title | None | 250 | 21.0 |
| Primary | 388 | 32.5 | |
| Junior | 422 | 35.4 | |
| Deputy senior | 106 | 8.9 | |
| Senior | 27 | 2.3 | |
| Geographic region | Northern China | 441 | 36.9 |
| Southern China | 752 | 63.3 |
*Unmarried include single, separated, divorced and widowed.
Descriptive statistics
The respondents reported agreement on seven variables. Specifically, the mean±SD scores for PTG, moral sensitivity and service behaviour were 72.63±21.67, 45.38±8.13 and 35.17±5.88, respectively. Additionally, positive and significant correlations were observed among the variables, ranging from r=0.30 to 0.97 (all p<0.01). Detailed results of the correlation, mean and SD of the study variables are presented in online supplemental table 1.
The structural equation modelling (SEM) results
The measurement model
All factor loadings for PTG, moral sensitivity and service behaviour indicators exceeded 0.5, except for two items related to moral burden that fell below the threshold and were subsequently removed. Cronbach’s alpha values for all subscales ranged from 0.701 to 0.953, indicating satisfactory reliability. CR values for all latent variables varied from 0.695 to 0.956, confirming their internal consistency. The AVE values, ranging from 0.533 to 0.814, affirmed the convergent validity of the measurement model. Detailed results of reliability and validity are presented in online supplemental table 2. Initially, the measurement model showed a poor fit with χ 2/df at 17.57, GFI at 0.92, RFI at 0.94 and RMSEA at 0.12. Given Mardia’s coefficient of 86.23 (p<0.01), indicating multivariate non-normality, we employed Byrne’s34 method to calculate bootstrap-adjusted SEM fit indices for non-normal data. The adjusted fit indices demonstrated an excellent fit, with χ 2/df at 1.59, GFI at 1.00, RFI at 0.99, RMSEA at 0.02 and Bollen-Stine p<0.01.
The structural model
Figure 2 illustrates that PTG significantly and positively influences both moral sensitivity (β=0.517, p<0.01) and service behaviour (β=0.172, p<0.01). Additionally, moral sensitivity has a significant effect on service behaviour (β=0.644, p<0.01). Moral sensitivity also serves as a mediator between PTG and service behaviour, with a mediating effect size of β=0.333 (p<0.01). For hypothesis testing, the bootstrapping method with 1000 self-weighting samples confirmed that the 95% CI of the path coefficient ranged from 0.286 to 0.380, indicating partial mediation by moral sensitivity. The total effect of PTG on service behaviour was significant (β=0.506, p<0.01), with moral sensitivity accounting for 65.8% of this effect. The model explained 56.0% of the variance in service behaviour, demonstrating a moderate level of explanatory power.18The direct and indirect effects within the SEM are detailed in table 2.
Table 2
Direct and indirect effects for the structural equation modelling (SEM) model
| Path | B | β | SE | P value | 95% CI |
| Direct effect | |||||
| Post-traumatic growth → service behaviour | 0.131 | 0.172 | 0.037 | 0.001 | (0.115, 0.236) |
| Indirect effect | |||||
| Post-traumatic growth → moral sensitivity → service behaviour | 0.253 | 0.333 | 0.029 | 0.002 | (0.286, 0.380) |
| Total effect | |||||
| Post-traumatic growth → service behaviour | 0.384 | 0.506 | 0.033 | 0.002 | (0.450, 0.559) |
B, unstandardised beta; β, standardised beta.
Discussion
Levels of post-traumatic growth (PTG), moral sensitivity and service behaviour
The current study reveals that HCWs experienced moderate-to-high levels of PTG in the post-COVID-19 pandemic era, with an average PTG score of 72.63±21.67. This finding indicates significant PTG among HCWs, surpassing the levels reported previously.38 A three-wave follow-up study in mainland China supports this observation, suggesting a time-dependent increase in HCWs’ PTG levels post-trauma.39 Additionally, a review of longitudinal mental health studies among HCWs during COVID-19 mirrors this positive trend in psychological outcomes,40 consistent with findings from previous outbreak studies.41 42 Notably, the high PTG levels in our study, conducted in the post-pandemic period, may be attributed to factors such as increased experience in patient management, psychological adaptation, public and familial social support, and sustained positive coping mechanisms.43 It is important to recognise the dual nature of PTG, as highlighted by Kadri et al,44 which exists in parallel with negative mental health outcomes. PTG aids in adjustment to new circumstances but may also exacerbate issues like denial, avoidance and severe mental health disorders such as depression and anxiety.45 Hence, understanding the complexity of PTG in HCWs is essential.
The study further explores moral sensitivity among HCWs. The mean moral sensitivity score was 45.38±8.13, notably higher than the scores in studies by He et al 46 on psychiatric nurses and Li et al 47 on Chinese paediatric nurses. The COVID-19 pandemic has undeniably heightened ethical challenges for healthcare professionals globally, impacting their practice and patient care quality.48 In our study, most HCWs in the post-pandemic era demonstrated strong ethical sensitivity and were equipped to recognise moral challenges, handle clinical pressures, adhere to professional ethics, and meet patients’ physiological and psychological needs. However, issues such as burnout and consequences of moral conflict, including delayed treatments and inappropriate care, may undermine HCWs’ respect and reputation.49 Huang et al 50 identified several barriers to moral sensitivity, including limited work experience, lack of ethical knowledge, conformist attitudes and hierarchical work environments. Addressing these challenges is crucial for health system policymakers to enhance the ethical performance and overall work quality of HCWs. This nuanced understanding of moral sensitivity complements our study’s findings on HCWs’ service behaviour.
The study also evaluated HCWs’ service behaviour, finding it to be above average.7 32 Most HCWs met the basic standards of medical service quality, yet there was a noticeable discrepancy between ISB and ESB, with the latter being lower. This observation aligns with previous studies indicating that, while HCWs often voluntarily offer additional services to meet patient needs, their initiative diminishes for tasks beyond their job scope.32 In the competitive healthcare market, hospitals must leverage advanced technology, management and comprehensive medical services to maintain their edge.7 Factors such as marital status, work experience, organisational ethics, job performance and attitudes toward job satisfaction training significantly influence both ISB and ESB in nurses.32 51 Enhancing HCWs’ service behaviour through effective organisational management and education is pivotal in the post-pandemic era to improve patient satisfaction, safety and hospital competitiveness. These multifaceted challenges and responsibilities faced by HCWs in the post-pandemic era underscore the need for comprehensive policies and practices to support their professional development and well-being.
The correlations of post-traumatic growth (PTG, moral sensitivity and service behaviour
This study confirms a positive correlation between PTG and moral sensitivity in HCWs, supporting our hypotheses. The COVID-19 pandemic, persisting for 3 years in China, has had significant worldwide effects, resulting in both positive and negative psychological impacts on HCWs 39. Environmental stressors compel individuals to use personal resources, such as emotional expression and resilience. This proactive engagement enhances their capacity to identify moral issues, comprehend their responsibilities and make optimal patient care decisions.20
In line with existing research,7 52 our study confirms a positive association between moral sensitivity and the service behaviour of HCWs. Service behaviour in the healthcare sector is inherently an ‘ethically laden practice’ where moral sensitivity plays a fundamental role.7 This heightened sensitivity steers HCWs’ attention to ethical considerations in both standard and ESBs.53 54 HCWs with elevated moral sensitivity tend to be more committed to their professional duties, adhering to procedures and rules meticulously.52 Moreover, an increase in moral sensitivity encourages HCWs to go beyond formal job requirements, driven by an understanding that the public and patients expect such ESBs. This enhanced moral sensitivity in HCWs, as a result of PTG, suggests a potential positive impact on their service behaviour.
Additionally, this study found that PTG directly and positively predicts HCWs’ service behaviour. Higher levels of PTG are associated with increased moral sensitivity, which in turn enhances service behaviour. The COVID-19 pandemic, a unique form of mass trauma, potentially catalyses positive changes in individuals.55 HCWs involved in critical patient care activities are likely to experience accelerated PTG in response to these challenges.55 This rapid development fosters increased personal strength, shifts in priorities, a deeper appreciation of life and a richer spiritual understanding. Consequently, HCWs are better equipped to provide superior medical care, maintain resilience and nurture beneficial patient relationships,56 thereby enhancing their capacity to handle ongoing challenges.
Moreover, PTG not only directly impacts HCWs’ service behaviour but also exerts an indirect influence through enhanced moral sensitivity, illustrating PTG’s extensive impact on both individual resilience and professional behaviour in the post-pandemic landscape. Recognising these effects, policymakers and healthcare administrators must address the mental and moral health of HCWs, alongside their material and spiritual needs. Implementing targeted mental health interventions and moral-ethical training is crucial for improving HCWs’ well-being and effectiveness, which contributes to the sustainable development of healthcare systems. The widespread impact of the COVID-19 pandemic highlights the relevance of our findings, suggesting that similar future health crises could pose comparable challenges, potentially affecting PTG, moral sensitivity and service behaviours in similar ways. The resilience and ethical responsiveness exhibited by Chinese HCWs in the post-pandemic era offer valuable insights for managing public health emergencies and supporting HCWs globally, underscoring the importance of preparedness for future crises and the global applicability of these findings.
Limitations
This study had several limitations. First, its cross-sectional design, chosen to capture interactions among key variables in HCWs within a specified timeframe, cannot establish causal relationships but provides valuable insights. Second, the recruitment strategy of convenience and snowball sampling might limit sample representativeness, yet it allowed for gathering a diverse cohort of over 1000 HCWs, laying a solid groundwork for examining associations. Thirdly, the reliance on self-reported measures risks introducing response biases, notably social desirability bias, which could inflate reported levels of moral sensitivity and service behaviour. Nonetheless, self-reports were critical for accessing participants’ subjective perceptions, crucial to the analysis of these constructs. Future research should address the impacts of social desirability bias and refine statistical methods to improve the accuracy of interpretations. Finally, to improve model fit, we eliminated two items from the moral sensitivity scale, which may compromise the completeness of the scale. However, this decision was justified by their low factor loadings and the significant improvement in the overall model fit following their exclusion.
Conclusions
This study examined the relationships among PTG, moral sensitivity and HCWs’ service behaviour in the post-pandemic era. The findings reveal that both PTG and moral sensitivity exert a positive effect on HCWs’ service behaviour. Moreover, the research establishes that PTG, induced by the COVID-19 pandemic, augments the moral sensitivity of HCWs, which in turn influences their service behaviour. Enhancing PTG and moral sensitivity contributes to better service behaviour among HCWs. These results emphasise the importance of targeted mental health interventions and ethical training for HCWs to effectively prepare for future crises.
The authors would like to thank Nanjing Medical University and Anhui Medical University for supporting this work. The authors express gratitude to the editor and reviewers for their valuable comments and thank all participants for accepting participation in the research study.
Data availability statement
Data are available upon reasonable request. The data that support the findings of this study are available from the corresponding author upon reasonable request.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study involves human participants and was approved by the Ethics Committee of Nanjing Medical University (NMU2019-716). Participants gave informed consent to participate in the study before taking part.
Contributors DL is the guarantor. All authors contributed to the design of the study. LW conducted the statistical analyses and drafted the manuscript. DL was responsible for obtaining funding, critically revising the manuscript for significant intellectual content and approving the final version for submission. LH, KW, QW, HZ, MW, XC, HW, FY, Mp, XL, LH, ZX and YZ were involved in data collection and also participated in revising the manuscript. HF provided critical revisions to the manuscript for significant intellectual content.
Funding This work was funded by the 'Anhui Zhongji Guoyi Medical Technology Co., Ltd' Open Program of Hospital Management Institute, Anhui Medical University (2022gykj03), the National Natural Science Foundation of China (72374109) and the 2022 CMB Open Competition Program (22-475).
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
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