Heterogeneity of Moral Injury and Its Pathway to Negative Emotions in Medical Staff: The Differential Mediating Role of Emotion Regulation

Heterogeneity of Moral Injury and Its Pathway to Negative Emotions in Medical Staff: The Differential Mediating Role of Emotion Regulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Heterogeneity of Moral Injury and Its Pathway to Negative Emotions in Medical Staff: The Differential Mediating Role of Emotion Regulation Runkang Zhao, Kai Zhang, Yiming Zhou, Kuiliang Li, Yuting Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9334719/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background Moral injury and negative emotions are core psychological issues that severely impair the mental health and professional competence of medical staff, while emotion regulation serves as a crucial psychological resource for coping with occupational psychological distress. However, the latent heterogeneous characteristics of moral injury among medical staff, as well as the differential mediating role of emotion regulation between moral injury and negative emotions, have not yet been systematically revealed. Objective This study aimed to identify the latent subtypes of moral injury among medical staff via latent profile analysis and to explore the mediating role of emotion regulation and its core strategies between moral injury and negative emotions, so as to provide empirical evidence for formulating targeted psychological intervention strategies for medical staff. Methods This was a cross-sectional survey. 1001 medical staff were enrolled via a convenience sampling method. Questionnaires were used to assess moral injury, anxiety, depression and emotion regulation among medical staff, with latent profile analysis applied for subgroup identification. The relationships among variables were analyzed using independent-samples t-test, analysis of variance, Pearson correlation analysis, latent profile analysis and mediation analysis. Results Three profiles of moral injury were identified and designated as the Lowest (11.39%), Med-low (74.52%), and Highest (14.09%) groups. Significant associations were observed among emotion regulation, moral injury, and negative emotions. Emotion regulation exerted a significant mediating effect on the relationship between moral injury and depression, as well as anxiety; the two subscales of emotion regulation (Cognitive Reappraisal and Expressive Suppression) were also found to play similar mediating roles. Conclusion This study offers recommendations for developing psychological interventions and management strategies to mitigate negative emotions in medical staff. Given that moral injury and emotion regulation significantly influence medical staff’ negative emotions, administrators should account for individual differences in moral injury. For medical staff with high moral injury, priority should be given to guiding adaptive emotion regulation strategies, with an emphasis on cognitive reappraisal, to alleviate negative emotions and safeguard nursing care quality. medical staff moral injury negative emotions emotion regulation latent profile analysis mediation analysis Figures Figure 1 Figure 2 Figure 3 1. Introduction Moral injury (MI) is defined as persistent psychosocial and spiritual impairment triggered by exposure to high-stakes events. Such events entail the violation of an individual’s deeply ingrained moral values and beliefs about right and wrong—whether through one’s own actions, inaction, or the actions of others—or the perceived betrayal by figures in positions of authority or trust [1]. Events with the potential to induce MI are classified into three categories: acts of commission, acts of omission, and acts of betrayal [2]. Empirical research has indicated that core manifestations of MI include intense feelings of guilt, shame, and anger [3–5]. Theoretical frameworks postulate that these psychological disturbances can be chronic, as impaired meaning-making, self-reproach, and loss of trust disrupt the processing and integration of emotions, thereby perpetuating psychological distress [6–8]. Although MI is not a formally diagnosable mental disorder, numerous studies have established a robust link between MI and adverse mental health outcomes such as post-traumatic stress disorder (PTSD), depression, and suicidal ideation [3,9,10]. Distinct from other forms of psychological distress, MI is uniquely rooted in the transgression of personal moral tenets, which often co-occurs with psychological comorbidities and functional impairments, rendering it particularly persistent and refractory to intervention [11]. As frontline professionals tasked with the sacred duty of saving lives and relieving suffering, medical staff frequently encounter intricate ethical dilemmas in daily clinical practice. These scenarios include making difficult triage decisions amid scarce medical resources, allocating critical care resources such as ventilators and beds, witnessing unavoidable patient mortality despite exhaustive clinical efforts, navigating conflicts between professional duties and personal moral principles, and even being compelled to implement medical interventions that conflict with their core values. Collectively, these experiences render medical staff highly susceptible to MI. Studies have demonstrated that the prevalence of MI among medical staff remains consistently high, particularly amid public health emergencies (e.g., the COVID-19 pandemic) and high-pressure clinical settings characterized by prolonged working hours and heavy workloads [12,13]. MI exerts far-reaching consequences, manifesting in both psychological comorbidities and functional impairments. Psychologically, it frequently coexists with depression, anxiety, PTSD, and professional burnout, reflecting profound emotional distress and guilt stemming from morally challenging experiences. Beyond individual psychological symptoms, MI also imposes significant functional impacts [14], including diminished job satisfaction, impaired teamwork, and compromised professional performance and collaboration. Additionally, it can strain personal relationships due to emotional withdrawal or increased irritability in affected individuals [12,15]. Organizational factors such as inadequate resource allocation and poor leadership can exacerbate these adverse effects, further straining both the personal and professional lives of medical staff [16]. In summary, MI exerts a multifaceted impact on medical staff, influencing not only their mental health but also interpersonal dynamics and professional competence. Thus, investigating the characteristics and impacts of MI among medical staff is of great significance for enhancing their mental health and fostering the stable development of the medical industry. Negative emotion (NE) refers to a spectrum of unpleasant affective states elicited by adverse life events or interpersonal interactions, with anxiety and depression being the most common manifestations [17]. Medical staff regularly encounter a variety of potentially morally injurious events (PMIEs), such as the helpless observation of patient death [18,19] and confrontations with moral dilemmas related to treatment delays [20]. These events induce varying degrees of moral distress or injury [21,22], making medical staff particularly prone to experiencing a range of negative emotional states [22–24]. Anxiety is often accompanied by excessive worry regarding work performance, patient safety, and personal health, which may lead to restlessness, impaired concentration, and sleep disturbances. Depression, on the other hand, is characterized by persistent low mood, diminished interest in work and daily activities, and feelings of helplessness and hopelessness [25]. These negative emotions directly compromise the physical and mental health of medical staff, leading to symptoms such as depressed mood, anhedonia, guilt, feelings of worthlessness, sleep and appetite disturbances, fatigue, and poor concentration [26]. In turn, these impairments further exacerbate the burden of clinical work, reduce work efficiency, and hinder the sustainable development of the medical industry [26,27]. Given the high prevalence and severe adverse impacts of NE among medical staff, exploring its influencing factors and underlying mechanisms is imperative for clinical practice and healthcare management. In recent years, a growing body of empirical evidence has substantiated the association between MI and NE in professional populations. For instance, frontline health and social care workers during crises are often forced to make morally challenging decisions with limited knowledge and resources; in one such study, 33.6% of participants met the diagnostic criteria for major depressive disorder, 21.5% for generalized anxiety disorder, and 19.1% reported comorbid depression and anxiety [28]. Conceptually, PMIEs have been posited to profoundly undermine the moral frameworks of veterans, challenging their fundamental perceptions of right and wrong [29]. Veterans who fail to reconcile the psychological discomfort caused by their actions are at an increased risk of social condemnation or rejection, which may subsequently lead to depressive symptoms and other adverse emotional experiences [30]. However, the majority of existing studies adopt a variable-centered approach, which focuses on the overall correlation between the average levels of MI and NE while neglecting the potential heterogeneity of MI among medical staff [31,32]. In other words, medical staff may exhibit distinct MI profiles with varying severity and symptom characteristics, such as low, moderate, and high MI subgroups. The nature of the relationship between MI and NE may differ substantially across these subgroups-a nuance that cannot be captured by traditional variable-centered analytical approaches. Emotion regulation (ER) is defined as the conscious or unconscious cognitive and behavioral processes through which individuals modulate the intensity, duration, and expression of their emotional experiences to adapt to external environmental demands and internal psychological needs [33,34]. It constitutes a critical psychological resource for individuals to cope with negative emotions and psychological distress [35]. Two well-documented ER strategies in the literature are cognitive reappraisal (CR) and expressive suppression (ES), each exerting distinct effects on individual emotional states [36]. Extensive research has established a strong association between ER strategies and NE [37–39]. Compared with ES, CR-an adaptive and proactive ER strategy-is consistently associated with fewer negative emotional experiences and more favorable mental health outcomes [40]. Meta-analytic evidence further indicates a moderate negative correlation between CR and symptoms of anxiety and depression, suggesting that frequent use of CR can effectively reduce the risk of these affective disorders [41]. In contrast, ES = a maladaptive ER strategy = is often linked to increased negative emotional experiences, heightened psychological distress, and even the development of chronic mental health problems [42]. Furthermore, the association between ER and MI has been widely validated in previous research [43]. Psychological distress induced by MI can disrupt an individual’s ER processes, leading to the maladaptive use of ER strategies (e.g., over-reliance on ES instead of CR), which in turn exacerbates both MI symptoms and negative emotions. Conversely, effective ER can help individuals mitigate the psychological impact of MI, alleviate associated distress, and reduce the likelihood of developing NE. Based on these findings, we hypothesize that ER (including its two core subcomponents, CR and ES) plays a mediating role in the relationship between MI and NE (i.e., anxiety and depression) among medical staff. Latent profile analysis (LPA) is an individual-centered analytical method that identifies distinct subgroups of individuals based on patterns of variation across multiple indicator variables. It facilitates the identification of high-risk populations and the development of targeted intervention strategies [44]. To date, few studies have systematically explored the latent profiles of MI and their differential associations with negative emotional outcomes; existing research in this area has primarily focused on populations such as police officers and military veterans with PTSD [45]. Additionally, the underlying psychological mechanism linking MI to NE from the perspective of ER remains poorly understood. This research gap restricts our comprehensive understanding of the psychological pathways of MI and impedes the development of targeted and effective intervention measures for affected medical staff. To the best of our knowledge, the present study is the first to systematically examine the mediating role of ER in the relationship between MI and NE among medical staff, while simultaneously accounting for both the distinct latent profiles of MI and the differential effects of specific ER strategies (CR and ES). This study addresses the aforementioned research gaps by adopting a person-centered approach to identify latent MI profiles in a sample of medical staff and exploring the mediating effects of different ER strategies on the relationship between MI profiles and NE. This research design not only helps to elucidate the complex relationship between MI and NE but also provides a theoretical foundation for developing targeted mental health interventions for medical staff with different MI profiles. Based on the aforementioned empirical findings and theoretical analyses, we propose the following three research hypotheses: H1: The heterogeneity of MI among medical staff can be identified through LPA. H2: There are correlations among MI, anxiety, depression, and ER in medical staff. H3: ER (i.e., CR and ES) plays a mediating role in the relationship between MI and NE (i.e., anxiety and depression). The conceptual model of this study, which illustrates the hypothesized relationships between MI, ER (CR and ES), and NE (anxiety and depression), is shown in the Fig. 1 . Note Moral injury is the independent variable, Cognitive Reappraisal and Expressive Suppression are the mediating variables, and Negative Emotion (anxiety and depression) are the dependent variables 2. Methods 2.1 Participants Survey data were gathered through Wenjuanxing ( www.wjx.cn ), a widely used online questionnaire platform in China, during the period from April 10 to 21, 2025. The initial recruitment encompassed 1,425 healthcare practitioners from three Grade III Class A general hospitals-the top tier in China’s official classification system for public medical institutions-located in the southern region of China. Only participants who provided written informed consent were included in the study. A rigorous data cleaning process was subsequently conducted, which resulted in the exclusion of 424 completed questionnaires: 31 were discarded due to incomplete or inaccurate demographic information, and an additional 393 questionnaires were eliminated because respondents failed both of the two embedded attention-check questions (e.g., not following the explicit directive ‘Please select the third option for this item). Questionnaires were distributed to medical staff via the head of each department in the three hospitals, with a total of 1425 questionnaires distributed and 1001 valid questionnaires recovered, with an effective recovery rate of 70.24%. 2.2 Measures 2.2.1 General Information Questionnaire The self-designed questionnaire included demographic characteristics such as gender (male = 1, female = 2), age (continuous variable), work experience (continuous variable), occupation (doctor = 1, nurse = 2), marital status (unmarried = 1, married = 2, divorced = 3), educational level (technical secondary school = 1, college = 2, undergraduate = 3, postgraduate = 4), title (none = 1, primary = 2, intermediate = 3, associate senior = 4, senior = 5). 2.2.2 The Chinese Version of Moral Injury Symptom Scale The Moral Injury Symptom Scale for Health Professionals (MISS-HP) is a specialized tool for assessing moral injury (MI) symptoms in medical practitioners, with its evaluation dimensions including betrayal, guilt, shame, moral concerns, loss of trust, loss of meaning, difficulty forgiving, self-condemnation, religious struggle, and diminished religious or spiritual faith [46]. Comprising 10 items in total, each item is rated on a 10-point scale to indicate the degree of agreement with the relevant statement, generating a total score ranging from 10 to 100. Higher total scores denote a greater number and more severe presentation of MI symptoms [47]. Wang et al. translated the MISS-HP into a Chinese version, which was proven to have acceptable reliability and validity among healthcare professionals in mainland China, with a Cronbach’s α coefficient of 0.70 [48]. In the present study, the MISS-HP also demonstrated acceptable internal consistency for the study sample, with the Cronbach’s α coefficient at 0.74. 2.2.3 Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder Scale (GAD-7) Depressive symptoms were assessed utilizing the 9-item Patient Health Questionnaire-9 (PHQ-9) [49]. All items of the scale correspond to specific depressive symptom manifestations, with each item rated on a 4-point scale from 0 (not at all) to 3 (nearly every day). The total score of the PHQ-9 ranges from 0 to 27, with higher cumulative scores indicative of more severe depressive symptoms in participants. The PHQ-9 has been psychometrically validated and confirmed to have good reliability and validity for the assessment of depressive symptoms in the Chinese population [50], and in the present study, the Cronbach’s α coefficient of the PHQ-9 reached 0.88, demonstrating excellent internal consistency. For the measurement of anxiety symptoms, the 7-item Generalized Anxiety Disorder Scale (GAD-7) was adopted in this research [51]. This scale quantifies common anxiety symptom dimensions, with each item scored on a 4-point Likert scale (0 = not at all, 3 = nearly every day), yielding a total score ranging from 0 to 21; the total score is positively correlated with the severity of anxiety symptoms. The GAD-7 has been verified as a reliable and valid assessment tool for screening anxiety symptoms among Chinese medical staff [52]. In this study, the GAD-7 exhibited high internal consistency reliability, with a Cronbach’s α coefficient of 0.91. 2.2.4 Emotion Regulation Questionnaire (ERQ) Originally developed by Gross & John [53], the Emotion Regulation Questionnaire was subsequently adapted and revised to suit the Chinese population [54]. The scale consists of 10 items that fall into two core dimensions: Cognitive Reappraisal (CR), which contains 6 items, and Expressive Suppression (ES), comprising 4 items. All items are rated on a 7-point Likert scale, where higher scores represent a more frequent adoption of the corresponding emotion regulation strategy. The original version of the scale reported a Cronbach’s α coefficient of 0.85 for the CR subscale and 0.77 for the ES subscale. In the present study, the CR subscale showed good internal consistency reliability with a Cronbach’s α coefficient of 0.86, whereas the ES subscale achieved an acceptable level of internal consistency with a Cronbach’s α coefficient of 0.78. 2.3 Data Analysis First, descriptive statistics were carried out to outline the demographic and occupational attributes of the study sample, and Pearson’s correlation analysis was employed to explore the pairwise correlations among MI, ER and its subscales (CR, ES), as well as NE (anxiety, depression). Second, latent profile analysis (LPA) was utilized to identify distinct latent subgroups of medical staff according to their MI symptom scores, with sequential testing of 1 to 4-category profile models for this analysis. Model fit was assessed through a set of metrics: lower values of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sample-adjusted Bayesian Information Criterion (aBIC) represented better model fit; the entropy value was used to measure classification accuracy; the Lo-Mendell-Rubin (LMR) test was adopted to compare the fit of k and k-1 category models, where a significant p-value indicated the k-category model was more optimal. Finally, mediation analysis with the Bootstrap approach was conducted to examine the mediating effects of ER and its two subscales (CR, ES) on the association between MI latent profile membership and NE (anxiety, depression), with the low MI subgroup designated as the reference group. The statistical significance of all mediating effects was judged by the 95% Bootstrap confidence interval, with an effect deemed significant if zero was not included in the interval. All statistical analyses were performed with SPSS (version 26.0), PROCESS Macro (Model 4), and Mplus (version 8.3) software, while independent-samples t-test and analysis of variance were used as supplementary tests for examining intergroup differences in relevant variables. 3. Results 3.1 Sample Demographic Characteristics The final analytical sample comprised 439 physicians (240 females) and 562 nurses (527 females), with participants’ ages ranging from 19 to 75 years (M = 34.86, SD = 7.94) and professional working experience spanning 0 to 52 years (M = 12.11, SD = 8.42). Detailed descriptive statistics for the study sample are presented in Table 1 . Table 1 Demographic characteristics (N = 1001, %) Variables N Percentage Variables N Percentage Gender Male 234 43.86 Marital Status Married 329 32.87 Female 767 56.14 Unmarried 651 65.03 Identity Doctor 439 23.38 Others 21 2.10 Nurse 562 76.62 Educational Level College 286 28.57 Age ≤ 30 years 324 32.37 Bachelor 623 62.24 31–50 years 644 64.34 Postgraduate 92 9.19 ≥ 51 years 33 3.30 Professional Title Primary 512 51.15 Working Years 21 years 174 17.38 Senior 11 1.10 3.2 Latent Profile Analysis of MI As shown in Table 2 , the fit statistics and classification indices revealed the optimal potential profile model for moral injury. The LMR test indicated that each higher-profile model (2-profile, 3-profile, 4-profile) was significantly better fitted than the previous one (all P<0.001). However, considering the balance of model fit and parsimony, the 3-profile model was identified as the optimal solution: it demonstrated a relatively high entropy value (0.89) indicating accurate classification, and its AIC, BIC, and aBIC values were reasonably low while avoiding overfitting compared to the 4-profile model. The 3-profile model consisted of three distinct groups: the lowest moral injury profile (n = 114, 11.39%), the medium moral injury profile (n = 746, 74.52%), and the highest moral injury profile (n = 141, 14.09%). Figure 2 shows that the average scores of all MI items increased gradually from the Lowest to the Highest MI group, indicating a clear gradient of MI severity across subgroups. Table 2 Fitting index and group size of latent profile analysis models Indices Unconditional Model 1-profile 2-profile 3-profile 4-profile Fit statistics LL -12486.3 -12215.7 -12028.9 -11912.4 AIC 24992.6 24461.4 24087.8 23864.8 BIC 25023.1 24507.5 24149.6 23942.2 aBIC 25001.8 24478.3 24106.5 23885.3 Entropy - 0.82 0.89 0.87 BLRT - 541.2 373.6 233.0 LMR - < 0.001 < 0.001 < 0.001 Group-sizes C1 100.00% 15.58% 11.39% 9.79% C2 - 84.42% 74.52% 32.57% C3 - - 14.09% 47.65% C4 - - - 9.99% 3.3 Meditation Analysis of ER Between MI Profiles and NE Statistical analyses revealed significant intercorrelations among ER, MI and NE, with detailed results presented in Table 3 . Pearson correlation analysis showed that MI was significantly positively correlated with depression ( p < 0.01 ) and anxiety ( p < 0.01 ); CR was significantly negatively correlated with MI and NE, while ES was significantly positively correlated with MI and NE (all p < 0.01 ). Based on the results of latent profile analysis, the low MI subgroup was designated as the reference group for mediating effect tests. For the LPM-ER-depression pathway, the mediating effect values were 0.316 (Med-low MI vs Lowest MI) and 0.724 (Highest MI vs Lowest MI); for the LPM-ER-anxiety pathway, the mediating effect values were0.291 (Med-low MI vs Lowest MI) and 0.668 (Highest MI vs Lowest MI). The 95% Bootstrap confidence intervals for these effects were (0.228 ~ 0.404), (0.569 ~ 0.879), (0.209 ~ 0.373) and (0.584 ~ 1.008), with none of the intervals containing zero, which confirmed the statistical significance of all identified mediating effects. The mediating effects of ER and its two subscales are further presented in Table 4 , and the specific mediating mechanisms are illustrated in Fig. 3 . Table 3 The level and association of medical staff’s MI with ER and NE Variables Correlation Matrix Mean SD 1 2 3 4 5 6 1.MI 45.86 14.32 1.00 2.ER 48.95 12.56 -0.24** 1.00 3.CR 22.67 4.12 -0.21** 0.85** 1.00 4.ES 22.65 4.31 0.15** -0.42** -0.56** 1.00 5.Depression 8.97 2.98 0.41** -0.31** -0.35** 0.29** 1.00 6.Anxiety 10.82 3.35 0.38** -0.28** -0.30** 0.25** 0.63** 1 Note. **correlation is significant at the 0.01 level (2-tailed) Table 4 The mediating effect of ER (categorical variable) on NE Indirect effect Effect (95%CI) 1 vs.2 Effect (95%CI) 1 vs.3 LPM-ER-Depression 0.316 (0.228 ~ 0.404) ** 0.724 (0.569 ~ 0.879) ** LPM-CR-Depression 0.118 (0.076 ~ 0.160) ** 0.274 (0.192 ~ 0.356) ** LPM-ES-Depression 0.198 (0.139 ~ 0.257) ** 0.448 (0.332 ~ 0.564) ** LPM- ER -Anxiety 0.291 (0.209 ~ 0.373) ** 0.668 (0.526 ~ 0.810) ** LPM- CR -Anxiety 0.105 (0.066 ~ 0.144) ** 0.247 (0.169 ~ 0.325) ** LPM- ES -Anxiety 0.186 (0.129 ~ 0.243) ** 0.421 (0.314 ~ 0.528) ** Note. LPM=latent profile membership of MI 4. Discussion The present study systematically explored the relationships among MI, ER, and NE (anxiety and depression) in medical staff, and verified the mediating role of two ER strategies in the link between MI and NE. The results confirmed all three research hypotheses, revealing significant correlations among the core variables, distinct latent subgroups of MI in medical staff with specific proportional characteristics, and the differential mediating effects of CR and ES with quantifiable effect values. These findings deepen the understanding of the psychological mechanisms underlying MI-induced NE in medical staff and provide a targeted theoretical basis for clinical psychological intervention and human resource management in the medical field. 4.1 Three Latent Subgroups of MI in Medical Staff via Latent Profile Analysis Hypothesis 1 was verified in this study, and three latent subgroups of moral injury (MI) in medical staff were identified by LPA from a person-centered perspective, namely the lowest MI group (11.39%, n = 114), the med-low MI group (74.52%, n = 746) and the highest MI group (14.09%, n = 141). This result reflects the significant heterogeneity of MI in the medical staff group, which is consistent with the conclusions of existing studies on occupational psychological characteristics based on LPA [55]. Different from the traditional variable-centered research that only focuses on the overall average level of MI, this study reveals that medical staff experience different degrees and symptom combinations of MI in clinical practice through individual-centered analysis, and the extreme polarization of MI severity is not prominent in this population. The distinct proportional distribution of MI subgroups is jointly shaped by individual and organizational factors, and the 74.52% proportion of the med-low MI group is the most prominent characteristic of MI in medical staff. On the individual level, medical staff receive systematic professional ethics training and clinical skill education during their career development [56,57], and most have formed a certain level of psychological resilience to cope with common clinical moral injury [58]; on the organizational level, regular medical institutions have basic resource allocation systems and ethical consultation mechanisms, which can reduce the occurrence of severe morally injurious events such as unavoidable patient death due to extreme resource scarcity. In clinical practice, most medical staff mainly face minor treatment delays, temporary resource tension and mild conflicts between professional duties and personal morality, which may be the proper reason for the highest proportion of the med-low MI group. In contrast, the highest MI group only accounts for 14.09% because severe MI is mostly triggered by rare high-stakes events, and the lowest MI group is a small number of medical staff with extremely strong psychological adjustment ability and few exposures to moral dilemmas. Notably, the proportional distribution of MI subgroups in medical staff is significantly different from that in other high-risk occupational groups. Compared with military veterans and trauma-exposed police officers, in whom the MI-PTSD subgroup accounts for 33.18% [45], the proportion of the highest MI group in medical staff is much lower. This difference may be due to the fact that medical staff receive regular professional psychological training and have a relatively complete clinical support system in their daily work [59], while veterans and police officers are more likely to encounter extreme traumatic events that trigger severe MI and lack continuous on-the-job psychological intervention. This cross-group comparison highlights the unique characteristics of MI in medical staff and enriches the person-centered research on MI in different occupational populations. The distribution characteristics of MI subgroups provide a critical basis for the risk stratification of medical staff's mental health, and the med-low MI group, as the absolute majority, is the key population for routine mental health screening in hospitals. This group is in a "mild to moderate MI state" for a long time, and although there is no immediate risk of severe NE, long-term clinical pressure, repeated exposure to mild moral dilemmas and lack of targeted intervention may lead to the progression of MI severity and the emergence of obvious anxiety and depressive symptom. The highest MI group, accounting for 14.09% of the sample, is the core population for intensive targeted psychological intervention, as this group faces the highest risk of developing severe NE. This finding makes up for the limitation that traditional variable-centered studies ignore individual differences in MI [60,61], breaks through the research perspective of only focusing on the overall correlation between MI and NE, and enables a more accurate understanding of the correlation law between them, laying a classification foundation for subsequent targeted psychological interventions. 4.2 Significant Associations were Observed among MI, Anxiety, Depression, and ER Hypothesis 2 was supported, and significant correlations were found among MI, anxiety, depression and ER strategies in the study sample, which is consistent with the conclusions of existing empirical research [62]. Medical staff with higher MI levels have more severe anxiety and depressive symptoms. The fundamental reason is that morally injurious events break the core moral convictions of medical staff, leading to strong feelings of guilt, shame, and anger [2,5]. These negative cognitive and emotional experiences further disrupt the individual's emotional processing and integration process, and ultimately manifest as clinical NE such as anxiety and depression [28,63]. This finding is consistent with the research conclusion on veterans and emergency frontline workers, that is, MI is an important risk factor for the occurrence of NE [64,65]. For medical staff who take saving lives and relieving pains as their sacred responsibility, the moral conflict and self-blame caused by MI are more intense, so the correlation between MI and NE is more significant in this population. ER, as a key psychological bridge connecting MI and NE, shows differentiated correlation characteristics in its two core sub-dimensions. CR is significantly negatively correlated with MI and NE, while ES is significantly positively correlated with both, which verifies the adaptive characteristics of CR and the maladaptive characteristics of ES in the field of medical staff's mental health [66,67]. This result is consistent with the conclusion of Gross & John's classic research on ER strategies [53], and further expands its application scenario to the specific context of medical staff's MI. Different ER forms play completely different roles in the emotional response process of medical staff facing MI: CR can reduce the emotional impact of MI by reconstructing the cognitive meaning of moral dilemmas, while ES will aggravate the accumulation of negative emotions caused by MI. This differentiated correlation becomes an important intermediate link for MI to affect NE, and also provides a solid theoretical basis for the subsequent exploration of the mediating effect of ER. 4.3 Discussion on the Mediation Results of ER between MI and NE The verification of Hypothesis 3 is the core finding of this study, which clarifies that CR and ES have differential mediating effects in the association between MI and NE with quantifiable effect values, and the two act as protective and maladaptive mediating variables respectively, jointly affecting the transformation process of MI to NE. This conclusion enriches the research on the mechanism of MI affecting mental health [68] and further clarifies the specific role of different ER strategies in this mechanism. CR plays a protective mediating role in the relationship between MI and anxiety and depression. That is, MI indirectly reduces the severity of anxiety and depressive symptoms of medical staff by promoting their use of CR strategies. As an active and adaptive ER strategy, CR can help reconstruct the cognitive meaning of life events [69,70], re-interpret clinical moral dilemmas from a more rational perspective, alleviate the negative emotional arousal caused by moral conflict and self-condemnation, and thus effectively buffer the negative impact of MI on NE. This finding not only confirms the meta-analytic evidence that CR is negatively correlated with anxiety and depression [41], but also extends its application scenario to the specific field of medical staff's MI, and clearly defines its emotional protection value in this special context. ES exerts a maladaptive mediating effect in the relationship between MI and NE. MI enhances the tendency of medical staff to use ES strategies, which in turn exacerbates their anxiety and depressive symptoms. ES is characterized by the conscious inhibition of emotional expression. In the context of MI, excessive use of this strategy will lead to the accumulation of negative emotions such as guilt and shame of medical staff, hinder the normal release and processing of emotions, and ultimately further aggravate psychological distress [71]. What is noteworthy is that the mediating effect values of both CR and ES show an increasing trend with the improvement of MI severity, which indicates that the role of ER strategies in the MI-NE relationship is more prominent in medical staff with higher MI levels, and also further confirms the necessity of targeted ER intervention for different MI subgroups. This differentiated mediating effect reveals the core role of ER strategy selection in the psychological consequences of MI, and also explains the reason why medical staff have completely different individual emotional responses when facing similar morally injurious events [72,73]. Medical staff who tend to use CR can effectively alleviate the negative impact of MI, while those who rely on ES will fall into a vicious circle of emotional accumulation and psychological distress. At the same time, it also indicates that the selection of ER strategies for medical staff is a key entry point for intervening in NE caused by MI. Guiding medical staff to use adaptive ER strategies and reducing the over-reliance on maladaptive ER strategies can effectively block the transmission of MI to NE. Combined with the mediating effect results and the characteristics of MI subgroups, the psychological intervention and human resource management for medical staff can form a three-level hierarchical practical strategy with strong pertinence and operability, and different intervention measures are formulated for different MI subgroups while distinguishing the professional characteristics of doctors and nurses: (1) Carry out regular MI cognitive education and CR basic training. For the training content, doctors focus on CR training in clinical diagnosis and treatment decision-making, while nurses focus on CR training in nurse-patient communication and clinical nursing operations. (2) Take med-low MI group (the majority) as the key population for routine mental health screening, carry out a comprehensive assessment of MI and NE every 6 months, and conduct short-term ER guidance, so as to prevent the progression of MI severity and achieve early intervention and early improvement. (3) Prioritize providing intensive psychological intervention for the highest MI group, such as 8 weeks of cognitive behavioral therapy (CBT) implemented by full-time psychologists in the hospital. 5 Limitations This study has several inherent limitations that should be acknowledged. First, the cross-sectional design only allows for the analysis of correlational and mediating relationships among moral injury (MI), emotion regulation (ER), and negative emotions (NE), precluding causal inferences between these variables. Future longitudinal studies with multiple time points are needed to verify the causal mediating mechanisms underlying the MI-ER-NE relationship. Second, this study focused solely on two classic ER strategies, namely cognitive reappraisal (CR) and expressive suppression (ES), and did not incorporate other adaptive strategies (e.g., acceptance and mindfulness) that can alleviate occupational psychological distress. Subsequent research should include a broader range of ER strategies to comprehensively explore their mediating and moderating roles in the association between MI and NE. Third, MI was assessed exclusively via self-report scales, which are susceptible to social desirability bias—medical staff may underreport their MI symptoms due to professional identity and work pressure. Future studies should adopt a multi-source assessment approach (combining self-reports, colleague evaluations, and supervisor evaluations) to improve the validity of MI measurement. 6 Conclusion This study explored the links between MI, ER (CR and ES) and NE (anxiety and depression) via latent profile analysis and mediation analysis. Three latent MI subgroups (low, moderate-low, high) were identified, confirming the heterogeneity of MI in this population, and the study further clarified the significant correlations among the core variables as well as the differential mediating effects of CR and ES in the MI-NE association. The findings enrich person-centered research on MI in medical staff and expand the application of the emotion regulation process model in this field by verifying the distinct mediating roles of CR and ES. They confirm MI as a critical risk factor for medical staff’s NE and ER strategy selection as a core coping element, providing targeted guidance for their mental health intervention and medical human resource management. Declarations Competing interests The authors declare no competing interests in this study. Ethical statement Approval for the data collection procedures was obtained from the Ethics Committee of the School of Psychology at Shaanxi Normal University (Approval No. HR2025-05-19), with all procedures conducted in accordance with the Declaration of Helsinki. Funding This study was funded by Dr. Lei Ren’s Start-up Fund (a special research initiation grant for new faculty) at Logistics University of PAP (HQXY-2025-BS-001). Author Contribution ZRK and ZK conceptualized the study, designed the research framework, and drafted the original manuscript. ZRK, ZK and ZYM completed the data collection and collation work. WYF and RL performed the statistical analysis and data interpretation. LKL and ZYT contributed to the critical revision of the manuscript for important intellectual content. All authors revised the manuscript critically, thoroughly reviewed the final draft, and approved its submission. All authors accept full responsibility for the entirety of the research presented in this study. Acknowledgements We would like to thank all the individuals who participated in the study. Data Availability The data from this study can be obtained by requesting it from the corresponding author. Due to privacy or ethical restrictions, the data is not publicly available. References Phelps AJ, Adler AB, Belanger SAH, et al. Addressing moral injury in the military. BMJ Mil Health . 2024;170(1):51-55. 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The response to challenging behaviour by care staff: emotional responses, attributions of cause and observations of practice. J Intellect Disabil Res . 2006;50(Pt 3):199-211. doi:10.1111/j.1365-2788.2005.00769.x Kebede MA, Demissie DB, Guddu DK, et al. Emotional responses and perceived stressors of frontline medical staffs in case of COVID-19 treatment centers and obstetrics emergency in Ethiopia. BMC Psychiatry . 2021;21(1):308. Published 2021 Jun 15. doi:10.1186/s12888-021-03311-1 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9334719","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633583352,"identity":"8103c250-e52b-412f-922b-04745f2a94ef","order_by":0,"name":"Runkang Zhao","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Runkang","middleName":"","lastName":"Zhao","suffix":""},{"id":633583353,"identity":"e7cc09bd-71ff-4b06-930c-ed232fe5060b","order_by":1,"name":"Kai Zhang","email":"","orcid":"","institution":"Air Force Medical 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Force","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2026-04-06 14:09:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9334719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9334719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108395109,"identity":"4de6fdd3-9453-4d58-a131-426e0e599640","added_by":"auto","created_at":"2026-05-04 07:50:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":225863,"visible":true,"origin":"","legend":"\u003cp\u003eThe conceptual model\u003c/p\u003e\n\u003cp\u003eNote: Moral injury is the independent variable, Cognitive Reappraisal and Expressive Suppression are the mediating variables, and Negative Emotion (anxiety and depression) are the dependent variables\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9334719/v1/55bb28d418fb1cb3a72039a4.png"},{"id":108395111,"identity":"3c07e3a7-ad8d-4213-9912-fcf07f07b532","added_by":"auto","created_at":"2026-05-04 07:50:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108234,"visible":true,"origin":"","legend":"\u003cp\u003eProbability of scoring for 3 potential profiles of medical staff’s MI\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9334719/v1/51162ff5c15cadf7f9acd650.png"},{"id":108395110,"identity":"9f291576-b5b5-48bd-a512-1a0f6bd83e27","added_by":"auto","created_at":"2026-05-04 07:50:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":841100,"visible":true,"origin":"","legend":"\u003cp\u003eThe mediating effect of ER (including its two subscales) on MI\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9334719/v1/7e3e187a866a4575d64e75da.png"},{"id":108395113,"identity":"1b576e01-661d-4475-85d1-a82a6058eb07","added_by":"auto","created_at":"2026-05-04 07:50:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1597757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9334719/v1/2172c128-7d59-4a57-a489-6f460ac1603d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heterogeneity of Moral Injury and Its Pathway to Negative Emotions in Medical Staff: The Differential Mediating Role of Emotion Regulation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMoral injury (MI) is defined as persistent psychosocial and spiritual impairment triggered by exposure to high-stakes events. Such events entail the violation of an individual\u0026rsquo;s deeply ingrained moral values and beliefs about right and wrong\u0026mdash;whether through one\u0026rsquo;s own actions, inaction, or the actions of others\u0026mdash;or the perceived betrayal by figures in positions of authority or trust [1]. Events with the potential to induce MI are classified into three categories: acts of commission, acts of omission, and acts of betrayal [2]. Empirical research has indicated that core manifestations of MI include intense feelings of guilt, shame, and anger [3\u0026ndash;5]. Theoretical frameworks postulate that these psychological disturbances can be chronic, as impaired meaning-making, self-reproach, and loss of trust disrupt the processing and integration of emotions, thereby perpetuating psychological distress [6\u0026ndash;8]. Although MI is not a formally diagnosable mental disorder, numerous studies have established a robust link between MI and adverse mental health outcomes such as post-traumatic stress disorder (PTSD), depression, and suicidal ideation [3,9,10].\u003c/p\u003e \u003cp\u003eDistinct from other forms of psychological distress, MI is uniquely rooted in the transgression of personal moral tenets, which often co-occurs with psychological comorbidities and functional impairments, rendering it particularly persistent and refractory to intervention [11]. As frontline professionals tasked with the sacred duty of saving lives and relieving suffering, medical staff frequently encounter intricate ethical dilemmas in daily clinical practice. These scenarios include making difficult triage decisions amid scarce medical resources, allocating critical care resources such as ventilators and beds, witnessing unavoidable patient mortality despite exhaustive clinical efforts, navigating conflicts between professional duties and personal moral principles, and even being compelled to implement medical interventions that conflict with their core values. Collectively, these experiences render medical staff highly susceptible to MI. Studies have demonstrated that the prevalence of MI among medical staff remains consistently high, particularly amid public health emergencies (e.g., the COVID-19 pandemic) and high-pressure clinical settings characterized by prolonged working hours and heavy workloads [12,13]. MI exerts far-reaching consequences, manifesting in both psychological comorbidities and functional impairments. Psychologically, it frequently coexists with depression, anxiety, PTSD, and professional burnout, reflecting profound emotional distress and guilt stemming from morally challenging experiences. Beyond individual psychological symptoms, MI also imposes significant functional impacts [14], including diminished job satisfaction, impaired teamwork, and compromised professional performance and collaboration. Additionally, it can strain personal relationships due to emotional withdrawal or increased irritability in affected individuals [12,15]. Organizational factors such as inadequate resource allocation and poor leadership can exacerbate these adverse effects, further straining both the personal and professional lives of medical staff [16]. In summary, MI exerts a multifaceted impact on medical staff, influencing not only their mental health but also interpersonal dynamics and professional competence. Thus, investigating the characteristics and impacts of MI among medical staff is of great significance for enhancing their mental health and fostering the stable development of the medical industry.\u003c/p\u003e \u003cp\u003eNegative emotion (NE) refers to a spectrum of unpleasant affective states elicited by adverse life events or interpersonal interactions, with anxiety and depression being the most common manifestations [17]. Medical staff regularly encounter a variety of potentially morally injurious events (PMIEs), such as the helpless observation of patient death [18,19] and confrontations with moral dilemmas related to treatment delays [20]. These events induce varying degrees of moral distress or injury [21,22], making medical staff particularly prone to experiencing a range of negative emotional states [22\u0026ndash;24]. Anxiety is often accompanied by excessive worry regarding work performance, patient safety, and personal health, which may lead to restlessness, impaired concentration, and sleep disturbances. Depression, on the other hand, is characterized by persistent low mood, diminished interest in work and daily activities, and feelings of helplessness and hopelessness [25]. These negative emotions directly compromise the physical and mental health of medical staff, leading to symptoms such as depressed mood, anhedonia, guilt, feelings of worthlessness, sleep and appetite disturbances, fatigue, and poor concentration [26]. In turn, these impairments further exacerbate the burden of clinical work, reduce work efficiency, and hinder the sustainable development of the medical industry [26,27]. Given the high prevalence and severe adverse impacts of NE among medical staff, exploring its influencing factors and underlying mechanisms is imperative for clinical practice and healthcare management.\u003c/p\u003e \u003cp\u003eIn recent years, a growing body of empirical evidence has substantiated the association between MI and NE in professional populations. For instance, frontline health and social care workers during crises are often forced to make morally challenging decisions with limited knowledge and resources; in one such study, 33.6% of participants met the diagnostic criteria for major depressive disorder, 21.5% for generalized anxiety disorder, and 19.1% reported comorbid depression and anxiety [28]. Conceptually, PMIEs have been posited to profoundly undermine the moral frameworks of veterans, challenging their fundamental perceptions of right and wrong [29]. Veterans who fail to reconcile the psychological discomfort caused by their actions are at an increased risk of social condemnation or rejection, which may subsequently lead to depressive symptoms and other adverse emotional experiences [30]. However, the majority of existing studies adopt a variable-centered approach, which focuses on the overall correlation between the average levels of MI and NE while neglecting the potential heterogeneity of MI among medical staff [31,32]. In other words, medical staff may exhibit distinct MI profiles with varying severity and symptom characteristics, such as low, moderate, and high MI subgroups. The nature of the relationship between MI and NE may differ substantially across these subgroups-a nuance that cannot be captured by traditional variable-centered analytical approaches.\u003c/p\u003e \u003cp\u003eEmotion regulation (ER) is defined as the conscious or unconscious cognitive and behavioral processes through which individuals modulate the intensity, duration, and expression of their emotional experiences to adapt to external environmental demands and internal psychological needs [33,34]. It constitutes a critical psychological resource for individuals to cope with negative emotions and psychological distress [35]. Two well-documented ER strategies in the literature are cognitive reappraisal (CR) and expressive suppression (ES), each exerting distinct effects on individual emotional states [36]. Extensive research has established a strong association between ER strategies and NE [37\u0026ndash;39]. Compared with ES, CR-an adaptive and proactive ER strategy-is consistently associated with fewer negative emotional experiences and more favorable mental health outcomes [40]. Meta-analytic evidence further indicates a moderate negative correlation between CR and symptoms of anxiety and depression, suggesting that frequent use of CR can effectively reduce the risk of these affective disorders [41]. In contrast, ES\u0026thinsp;=\u0026thinsp;a maladaptive ER strategy\u0026thinsp;=\u0026thinsp;is often linked to increased negative emotional experiences, heightened psychological distress, and even the development of chronic mental health problems [42]. Furthermore, the association between ER and MI has been widely validated in previous research [43]. Psychological distress induced by MI can disrupt an individual\u0026rsquo;s ER processes, leading to the maladaptive use of ER strategies (e.g., over-reliance on ES instead of CR), which in turn exacerbates both MI symptoms and negative emotions. Conversely, effective ER can help individuals mitigate the psychological impact of MI, alleviate associated distress, and reduce the likelihood of developing NE. Based on these findings, we hypothesize that ER (including its two core subcomponents, CR and ES) plays a mediating role in the relationship between MI and NE (i.e., anxiety and depression) among medical staff.\u003c/p\u003e \u003cp\u003eLatent profile analysis (LPA) is an individual-centered analytical method that identifies distinct subgroups of individuals based on patterns of variation across multiple indicator variables. It facilitates the identification of high-risk populations and the development of targeted intervention strategies [44]. To date, few studies have systematically explored the latent profiles of MI and their differential associations with negative emotional outcomes; existing research in this area has primarily focused on populations such as police officers and military veterans with PTSD [45]. Additionally, the underlying psychological mechanism linking MI to NE from the perspective of ER remains poorly understood. This research gap restricts our comprehensive understanding of the psychological pathways of MI and impedes the development of targeted and effective intervention measures for affected medical staff.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, the present study is the first to systematically examine the mediating role of ER in the relationship between MI and NE among medical staff, while simultaneously accounting for both the distinct latent profiles of MI and the differential effects of specific ER strategies (CR and ES). This study addresses the aforementioned research gaps by adopting a person-centered approach to identify latent MI profiles in a sample of medical staff and exploring the mediating effects of different ER strategies on the relationship between MI profiles and NE. This research design not only helps to elucidate the complex relationship between MI and NE but also provides a theoretical foundation for developing targeted mental health interventions for medical staff with different MI profiles. Based on the aforementioned empirical findings and theoretical analyses, we propose the following three research hypotheses:\u003c/p\u003e \u003cp\u003eH1: The heterogeneity of MI among medical staff can be identified through LPA.\u003c/p\u003e \u003cp\u003eH2: There are correlations among MI, anxiety, depression, and ER in medical staff.\u003c/p\u003e \u003cp\u003eH3: ER (i.e., CR and ES) plays a mediating role in the relationship between MI and NE (i.e., anxiety and depression).\u003c/p\u003e \u003cp\u003eThe conceptual model of this study, which illustrates the hypothesized relationships between MI, ER (CR and ES), and NE (anxiety and depression), is shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eMoral injury is the independent variable, Cognitive Reappraisal and Expressive Suppression are the mediating variables, and Negative Emotion (anxiety and depression) are the dependent variables\u003c/p\u003e \u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eSurvey data were gathered through Wenjuanxing (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.wjx.cn\u003c/span\u003e\u003cspan address=\"http://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a widely used online questionnaire platform in China, during the period from April 10 to 21, 2025. The initial recruitment encompassed 1,425 healthcare practitioners from three Grade III Class A general hospitals-the top tier in China\u0026rsquo;s official classification system for public medical institutions-located in the southern region of China. Only participants who provided written informed consent were included in the study. A rigorous data cleaning process was subsequently conducted, which resulted in the exclusion of 424 completed questionnaires: 31 were discarded due to incomplete or inaccurate demographic information, and an additional 393 questionnaires were eliminated because respondents failed both of the two embedded attention-check questions (e.g., not following the explicit directive \u0026lsquo;Please select the third option for this item). Questionnaires were distributed to medical staff via the head of each department in the three hospitals, with a total of 1425 questionnaires distributed and 1001 valid questionnaires recovered, with an effective recovery rate of 70.24%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 General Information Questionnaire\u003c/h2\u003e \u003cp\u003eThe self-designed questionnaire included demographic characteristics such as gender (male\u0026thinsp;=\u0026thinsp;1, female\u0026thinsp;=\u0026thinsp;2), age (continuous variable), work experience (continuous variable), occupation (doctor\u0026thinsp;=\u0026thinsp;1, nurse\u0026thinsp;=\u0026thinsp;2), marital status (unmarried\u0026thinsp;=\u0026thinsp;1, married\u0026thinsp;=\u0026thinsp;2, divorced\u0026thinsp;=\u0026thinsp;3), educational level (technical secondary school\u0026thinsp;=\u0026thinsp;1, college\u0026thinsp;=\u0026thinsp;2, undergraduate\u0026thinsp;=\u0026thinsp;3, postgraduate\u0026thinsp;=\u0026thinsp;4), title (none\u0026thinsp;=\u0026thinsp;1, primary\u0026thinsp;=\u0026thinsp;2, intermediate\u0026thinsp;=\u0026thinsp;3, associate senior\u0026thinsp;=\u0026thinsp;4, senior\u0026thinsp;=\u0026thinsp;5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 The Chinese Version of Moral Injury Symptom Scale\u003c/h2\u003e \u003cp\u003eThe Moral Injury Symptom Scale for Health Professionals (MISS-HP) is a specialized tool for assessing moral injury (MI) symptoms in medical practitioners, with its evaluation dimensions including betrayal, guilt, shame, moral concerns, loss of trust, loss of meaning, difficulty forgiving, self-condemnation, religious struggle, and diminished religious or spiritual faith [46]. Comprising 10 items in total, each item is rated on a 10-point scale to indicate the degree of agreement with the relevant statement, generating a total score ranging from 10 to 100. Higher total scores denote a greater number and more severe presentation of MI symptoms [47]. Wang et al. translated the MISS-HP into a Chinese version, which was proven to have acceptable reliability and validity among healthcare professionals in mainland China, with a Cronbach\u0026rsquo;s α coefficient of 0.70 [48]. In the present study, the MISS-HP also demonstrated acceptable internal consistency for the study sample, with the Cronbach\u0026rsquo;s α coefficient at 0.74.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder Scale (GAD-7)\u003c/h2\u003e \u003cp\u003eDepressive symptoms were assessed utilizing the 9-item Patient Health Questionnaire-9 (PHQ-9) [49]. All items of the scale correspond to specific depressive symptom manifestations, with each item rated on a 4-point scale from 0 (not at all) to 3 (nearly every day). The total score of the PHQ-9 ranges from 0 to 27, with higher cumulative scores indicative of more severe depressive symptoms in participants. The PHQ-9 has been psychometrically validated and confirmed to have good reliability and validity for the assessment of depressive symptoms in the Chinese population [50], and in the present study, the Cronbach\u0026rsquo;s α coefficient of the PHQ-9 reached 0.88, demonstrating excellent internal consistency.\u003c/p\u003e \u003cp\u003eFor the measurement of anxiety symptoms, the 7-item Generalized Anxiety Disorder Scale (GAD-7) was adopted in this research [51]. This scale quantifies common anxiety symptom dimensions, with each item scored on a 4-point Likert scale (0\u0026thinsp;=\u0026thinsp;not at all, 3\u0026thinsp;=\u0026thinsp;nearly every day), yielding a total score ranging from 0 to 21; the total score is positively correlated with the severity of anxiety symptoms. The GAD-7 has been verified as a reliable and valid assessment tool for screening anxiety symptoms among Chinese medical staff [52]. In this study, the GAD-7 exhibited high internal consistency reliability, with a Cronbach\u0026rsquo;s α coefficient of 0.91.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Emotion Regulation Questionnaire (ERQ)\u003c/h2\u003e \u003cp\u003eOriginally developed by Gross \u0026amp; John [53], the Emotion Regulation Questionnaire was subsequently adapted and revised to suit the Chinese population [54]. The scale consists of 10 items that fall into two core dimensions: Cognitive Reappraisal (CR), which contains 6 items, and Expressive Suppression (ES), comprising 4 items. All items are rated on a 7-point Likert scale, where higher scores represent a more frequent adoption of the corresponding emotion regulation strategy. The original version of the scale reported a Cronbach\u0026rsquo;s α coefficient of 0.85 for the CR subscale and 0.77 for the ES subscale. In the present study, the CR subscale showed good internal consistency reliability with a Cronbach\u0026rsquo;s α coefficient of 0.86, whereas the ES subscale achieved an acceptable level of internal consistency with a Cronbach\u0026rsquo;s α coefficient of 0.78.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Analysis\u003c/h2\u003e \u003cp\u003eFirst, descriptive statistics were carried out to outline the demographic and occupational attributes of the study sample, and Pearson\u0026rsquo;s correlation analysis was employed to explore the pairwise correlations among MI, ER and its subscales (CR, ES), as well as NE (anxiety, depression).\u003c/p\u003e \u003cp\u003eSecond, latent profile analysis (LPA) was utilized to identify distinct latent subgroups of medical staff according to their MI symptom scores, with sequential testing of 1 to 4-category profile models for this analysis. Model fit was assessed through a set of metrics: lower values of the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sample-adjusted Bayesian Information Criterion (aBIC) represented better model fit; the entropy value was used to measure classification accuracy; the Lo-Mendell-Rubin (LMR) test was adopted to compare the fit of k and k-1 category models, where a significant p-value indicated the k-category model was more optimal.\u003c/p\u003e \u003cp\u003eFinally, mediation analysis with the Bootstrap approach was conducted to examine the mediating effects of ER and its two subscales (CR, ES) on the association between MI latent profile membership and NE (anxiety, depression), with the low MI subgroup designated as the reference group. The statistical significance of all mediating effects was judged by the 95% Bootstrap confidence interval, with an effect deemed significant if zero was not included in the interval.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed with SPSS (version 26.0), PROCESS Macro (Model 4), and Mplus (version 8.3) software, while independent-samples t-test and analysis of variance were used as supplementary tests for examining intergroup differences in relevant variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample Demographic Characteristics\u003c/h2\u003e \u003cp\u003eThe final analytical sample comprised 439 physicians (240 females) and 562 nurses (527 females), with participants\u0026rsquo; ages ranging from 19 to 75 years (M\u0026thinsp;=\u0026thinsp;34.86, SD\u0026thinsp;=\u0026thinsp;7.94) and professional working experience spanning 0 to 52 years (M\u0026thinsp;=\u0026thinsp;12.11, SD\u0026thinsp;=\u0026thinsp;8.42). Detailed descriptive statistics for the study sample are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics (N\u0026thinsp;=\u0026thinsp;1001, %)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e65.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIdentity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducational Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eProfessional Title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWorking Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAssociate Senior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;21 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Latent Profile Analysis of MI\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the fit statistics and classification indices revealed the optimal potential profile model for moral injury. The LMR test indicated that each higher-profile model (2-profile, 3-profile, 4-profile) was significantly better fitted than the previous one (all P\u0026lt;0.001). However, considering the balance of model fit and parsimony, the 3-profile model was identified as the optimal solution: it demonstrated a relatively high entropy value (0.89) indicating accurate classification, and its AIC, BIC, and aBIC values were reasonably low while avoiding overfitting compared to the 4-profile model. The 3-profile model consisted of three distinct groups: the lowest moral injury profile (n\u0026thinsp;=\u0026thinsp;114, 11.39%), the medium moral injury profile (n\u0026thinsp;=\u0026thinsp;746, 74.52%), and the highest moral injury profile (n\u0026thinsp;=\u0026thinsp;141, 14.09%). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the average scores of all MI items increased gradually from the Lowest to the Highest MI group, indicating a clear gradient of MI severity across subgroups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFitting index and group size of latent profile analysis models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnconditional Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3-profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4-profile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFit statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-12486.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12215.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12028.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11912.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24992.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24461.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24087.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23864.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25023.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24507.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24149.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23942.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25001.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24478.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24106.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23885.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e541.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e373.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e233.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGroup-sizes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.65%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Meditation Analysis of ER Between MI Profiles and NE\u003c/h2\u003e \u003cp\u003eStatistical analyses revealed significant intercorrelations among ER, MI and NE, with detailed results presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Pearson correlation analysis showed that MI was significantly positively correlated with depression (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e) and anxiety (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e); CR was significantly negatively correlated with MI and NE, while ES was significantly positively correlated with MI and NE (all \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eBased on the results of latent profile analysis, the low MI subgroup was designated as the reference group for mediating effect tests. For the LPM-ER-depression pathway, the mediating effect values were 0.316 (Med-low MI vs Lowest MI) and 0.724 (Highest MI vs Lowest MI); for the LPM-ER-anxiety pathway, the mediating effect values were0.291 (Med-low MI vs Lowest MI) and 0.668 (Highest MI vs Lowest MI). The 95% Bootstrap confidence intervals for these effects were (0.228\u0026thinsp;~\u0026thinsp;0.404), (0.569\u0026thinsp;~\u0026thinsp;0.879), (0.209\u0026thinsp;~\u0026thinsp;0.373) and (0.584\u0026thinsp;~\u0026thinsp;1.008), with none of the intervals containing zero, which confirmed the statistical significance of all identified mediating effects. The mediating effects of ER and its two subscales are further presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and the specific mediating mechanisms are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe level and association of medical staff\u0026rsquo;s MI with ER and NE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eCorrelation Matrix\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.ER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.24**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.CR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.ES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.42**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.35**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.28**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.30**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. **correlation is significant at the 0.01 level (2-tailed)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe mediating effect of ER (categorical variable) on NE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect (95%CI) 1 vs.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect (95%CI) 1 vs.3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPM-ER-Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.316 (0.228\u0026thinsp;~\u0026thinsp;0.404) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.724 (0.569\u0026thinsp;~\u0026thinsp;0.879) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPM-CR-Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.118 (0.076\u0026thinsp;~\u0026thinsp;0.160) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.274 (0.192\u0026thinsp;~\u0026thinsp;0.356) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPM-ES-Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.198 (0.139\u0026thinsp;~\u0026thinsp;0.257) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.448 (0.332\u0026thinsp;~\u0026thinsp;0.564) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPM- ER -Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.291 (0.209\u0026thinsp;~\u0026thinsp;0.373) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.668 (0.526\u0026thinsp;~\u0026thinsp;0.810) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPM- CR -Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.105 (0.066\u0026thinsp;~\u0026thinsp;0.144) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.247 (0.169\u0026thinsp;~\u0026thinsp;0.325) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPM- ES -Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.186 (0.129\u0026thinsp;~\u0026thinsp;0.243) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.421 (0.314\u0026thinsp;~\u0026thinsp;0.528) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote. LPM=latent profile membership of MI\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study systematically explored the relationships among MI, ER, and NE (anxiety and depression) in medical staff, and verified the mediating role of two ER strategies in the link between MI and NE. The results confirmed all three research hypotheses, revealing significant correlations among the core variables, distinct latent subgroups of MI in medical staff with specific proportional characteristics, and the differential mediating effects of CR and ES with quantifiable effect values. These findings deepen the understanding of the psychological mechanisms underlying MI-induced NE in medical staff and provide a targeted theoretical basis for clinical psychological intervention and human resource management in the medical field.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Three Latent Subgroups of MI in Medical Staff via Latent Profile Analysis\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003ewas verified in this study, and three latent subgroups of moral injury (MI) in medical staff were identified by LPA from a person-centered perspective, namely the lowest MI group (11.39%, n\u0026thinsp;=\u0026thinsp;114), the med-low MI group (74.52%, n\u0026thinsp;=\u0026thinsp;746) and the highest MI group (14.09%, n\u0026thinsp;=\u0026thinsp;141). This result reflects the significant heterogeneity of MI in the medical staff group, which is consistent with the conclusions of existing studies on occupational psychological characteristics based on LPA [55]. Different from the traditional variable-centered research that only focuses on the overall average level of MI, this study reveals that medical staff experience different degrees and symptom combinations of MI in clinical practice through individual-centered analysis, and the extreme polarization of MI severity is not prominent in this population.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe distinct proportional distribution of MI subgroups is jointly shaped by individual and organizational factors, and the 74.52% proportion of the med-low MI group is the most prominent characteristic of MI in medical staff. On the individual level, medical staff receive systematic professional ethics training and clinical skill education during their career development [56,57], and most have formed a certain level of psychological resilience to cope with common clinical moral injury [58]; on the organizational level, regular medical institutions have basic resource allocation systems and ethical consultation mechanisms, which can reduce the occurrence of severe morally injurious events such as unavoidable patient death due to extreme resource scarcity. In clinical practice, most medical staff mainly face minor treatment delays, temporary resource tension and mild conflicts between professional duties and personal morality, which may be the proper reason for the highest proportion of the med-low MI group. In contrast, the highest MI group only accounts for 14.09% because severe MI is mostly triggered by rare high-stakes events, and the lowest MI group is a small number of medical staff with extremely strong psychological adjustment ability and few exposures to moral dilemmas.\u003c/p\u003e \u003cp\u003eNotably, the proportional distribution of MI subgroups in medical staff is significantly different from that in other high-risk occupational groups. Compared with military veterans and trauma-exposed police officers, in whom the MI-PTSD subgroup accounts for 33.18% [45], the proportion of the highest MI group in medical staff is much lower. This difference may be due to the fact that medical staff receive regular professional psychological training and have a relatively complete clinical support system in their daily work [59], while veterans and police officers are more likely to encounter extreme traumatic events that trigger severe MI and lack continuous on-the-job psychological intervention. This cross-group comparison highlights the unique characteristics of MI in medical staff and enriches the person-centered research on MI in different occupational populations.\u003c/p\u003e \u003cp\u003eThe distribution characteristics of MI subgroups provide a critical basis for the risk stratification of medical staff's mental health, and the med-low MI group, as the absolute majority, is the key population for routine mental health screening in hospitals. This group is in a \"mild to moderate MI state\" for a long time, and although there is no immediate risk of severe NE, long-term clinical pressure, repeated exposure to mild moral dilemmas and lack of targeted intervention may lead to the progression of MI severity and the emergence of obvious anxiety and depressive symptom. The highest MI group, accounting for 14.09% of the sample, is the core population for intensive targeted psychological intervention, as this group faces the highest risk of developing severe NE. This finding makes up for the limitation that traditional variable-centered studies ignore individual differences in MI [60,61], breaks through the research perspective of only focusing on the overall correlation between MI and NE, and enables a more accurate understanding of the correlation law between them, laying a classification foundation for subsequent targeted psychological interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Significant Associations were Observed among MI, Anxiety, Depression, and ER\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003ewas supported, and significant correlations were found among MI, anxiety, depression and ER strategies in the study sample, which is consistent with the conclusions of existing empirical research [62]. Medical staff with higher MI levels have more severe anxiety and depressive symptoms. The fundamental reason is that morally injurious events break the core moral convictions of medical staff, leading to strong feelings of guilt, shame, and anger [2,5]. These negative cognitive and emotional experiences further disrupt the individual's emotional processing and integration process, and ultimately manifest as clinical NE such as anxiety and depression [28,63]. This finding is consistent with the research conclusion on veterans and emergency frontline workers, that is, MI is an important risk factor for the occurrence of NE [64,65]. For medical staff who take saving lives and relieving pains as their sacred responsibility, the moral conflict and self-blame caused by MI are more intense, so the correlation between MI and NE is more significant in this population.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eER, as a key psychological bridge connecting MI and NE, shows differentiated correlation characteristics in its two core sub-dimensions. CR is significantly negatively correlated with MI and NE, while ES is significantly positively correlated with both, which verifies the adaptive characteristics of CR and the maladaptive characteristics of ES in the field of medical staff's mental health [66,67]. This result is consistent with the conclusion of Gross \u0026amp; John's classic research on ER strategies [53], and further expands its application scenario to the specific context of medical staff's MI. Different ER forms play completely different roles in the emotional response process of medical staff facing MI: CR can reduce the emotional impact of MI by reconstructing the cognitive meaning of moral dilemmas, while ES will aggravate the accumulation of negative emotions caused by MI. This differentiated correlation becomes an important intermediate link for MI to affect NE, and also provides a solid theoretical basis for the subsequent exploration of the mediating effect of ER.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Discussion on the Mediation Results of ER between MI and NE\u003c/h2\u003e \u003cp\u003eThe verification of Hypothesis 3 is the core finding of this study, which clarifies that CR and ES have differential mediating effects in the association between MI and NE with quantifiable effect values, and the two act as protective and maladaptive mediating variables respectively, jointly affecting the transformation process of MI to NE. This conclusion enriches the research on the mechanism of MI affecting mental health [68] and further clarifies the specific role of different ER strategies in this mechanism.\u003c/p\u003e \u003cp\u003eCR plays a protective mediating role in the relationship between MI and anxiety and depression. That is, MI indirectly reduces the severity of anxiety and depressive symptoms of medical staff by promoting their use of CR strategies. As an active and adaptive ER strategy, CR can help reconstruct the cognitive meaning of life events [69,70], re-interpret clinical moral dilemmas from a more rational perspective, alleviate the negative emotional arousal caused by moral conflict and self-condemnation, and thus effectively buffer the negative impact of MI on NE. This finding not only confirms the meta-analytic evidence that CR is negatively correlated with anxiety and depression [41], but also extends its application scenario to the specific field of medical staff's MI, and clearly defines its emotional protection value in this special context.\u003c/p\u003e \u003cp\u003eES exerts a maladaptive mediating effect in the relationship between MI and NE. MI enhances the tendency of medical staff to use ES strategies, which in turn exacerbates their anxiety and depressive symptoms. ES is characterized by the conscious inhibition of emotional expression. In the context of MI, excessive use of this strategy will lead to the accumulation of negative emotions such as guilt and shame of medical staff, hinder the normal release and processing of emotions, and ultimately further aggravate psychological distress [71]. What is noteworthy is that the mediating effect values of both CR and ES show an increasing trend with the improvement of MI severity, which indicates that the role of ER strategies in the MI-NE relationship is more prominent in medical staff with higher MI levels, and also further confirms the necessity of targeted ER intervention for different MI subgroups.\u003c/p\u003e \u003cp\u003eThis differentiated mediating effect reveals the core role of ER strategy selection in the psychological consequences of MI, and also explains the reason why medical staff have completely different individual emotional responses when facing similar morally injurious events [72,73]. Medical staff who tend to use CR can effectively alleviate the negative impact of MI, while those who rely on ES will fall into a vicious circle of emotional accumulation and psychological distress. At the same time, it also indicates that the selection of ER strategies for medical staff is a key entry point for intervening in NE caused by MI. Guiding medical staff to use adaptive ER strategies and reducing the over-reliance on maladaptive ER strategies can effectively block the transmission of MI to NE.\u003c/p\u003e \u003cp\u003eCombined with the mediating effect results and the characteristics of MI subgroups, the psychological intervention and human resource management for medical staff can form a three-level hierarchical practical strategy with strong pertinence and operability, and different intervention measures are formulated for different MI subgroups while distinguishing the professional characteristics of doctors and nurses: (1) Carry out regular MI cognitive education and CR basic training. For the training content, doctors focus on CR training in clinical diagnosis and treatment decision-making, while nurses focus on CR training in nurse-patient communication and clinical nursing operations. (2) Take med-low MI group (the majority) as the key population for routine mental health screening, carry out a comprehensive assessment of MI and NE every 6 months, and conduct short-term ER guidance, so as to prevent the progression of MI severity and achieve early intervention and early improvement. (3) Prioritize providing intensive psychological intervention for the highest MI group, such as 8 weeks of cognitive behavioral therapy (CBT) implemented by full-time psychologists in the hospital.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Limitations","content":"\u003cp\u003eThis study has several inherent limitations that should be acknowledged. First, the cross-sectional design only allows for the analysis of correlational and mediating relationships among moral injury (MI), emotion regulation (ER), and negative emotions (NE), precluding causal inferences between these variables. Future longitudinal studies with multiple time points are needed to verify the causal mediating mechanisms underlying the MI-ER-NE relationship. Second, this study focused solely on two classic ER strategies, namely cognitive reappraisal (CR) and expressive suppression (ES), and did not incorporate other adaptive strategies (e.g., acceptance and mindfulness) that can alleviate occupational psychological distress. Subsequent research should include a broader range of ER strategies to comprehensively explore their mediating and moderating roles in the association between MI and NE. Third, MI was assessed exclusively via self-report scales, which are susceptible to social desirability bias\u0026mdash;medical staff may underreport their MI symptoms due to professional identity and work pressure. Future studies should adopt a multi-source assessment approach (combining self-reports, colleague evaluations, and supervisor evaluations) to improve the validity of MI measurement.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study explored the links between MI, ER (CR and ES) and NE (anxiety and depression) via latent profile analysis and mediation analysis. Three latent MI subgroups (low, moderate-low, high) were identified, confirming the heterogeneity of MI in this population, and the study further clarified the significant correlations among the core variables as well as the differential mediating effects of CR and ES in the MI-NE association. The findings enrich person-centered research on MI in medical staff and expand the application of the emotion regulation process model in this field by verifying the distinct mediating roles of CR and ES. They confirm MI as a critical risk factor for medical staff\u0026rsquo;s NE and ER strategy selection as a core coping element, providing targeted guidance for their mental health intervention and medical human resource management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests in this study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical statement\u003c/h2\u003e \u003cp\u003eApproval for the data collection procedures was obtained from the Ethics Committee of the School of Psychology at Shaanxi Normal University (Approval No. HR2025-05-19), with all procedures conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by Dr. Lei Ren\u0026rsquo;s Start-up Fund (a special research initiation grant for new faculty) at Logistics University of PAP (HQXY-2025-BS-001).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZRK and ZK conceptualized the study, designed the research framework, and drafted the original manuscript. ZRK, ZK and ZYM completed the data collection and collation work. WYF and RL performed the statistical analysis and data interpretation. LKL and ZYT contributed to the critical revision of the manuscript for important intellectual content. All authors revised the manuscript critically, thoroughly reviewed the final draft, and approved its submission. All authors accept full responsibility for the entirety of the research presented in this study.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to thank all the individuals who participated in the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e The data from this study can be obtained by requesting it from the corresponding author. Due to privacy or ethical restrictions, the data is not publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePhelps AJ, Adler AB, Belanger SAH, et al. 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Published 2021 Jun 15. doi:10.1186/s12888-021-03311-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"medical staff, moral injury, negative emotions, emotion regulation, latent profile analysis, mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-9334719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9334719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMoral injury and negative emotions are core psychological issues that severely impair the mental health and professional competence of medical staff, while emotion regulation serves as a crucial psychological resource for coping with occupational psychological distress. However, the latent heterogeneous characteristics of moral injury among medical staff, as well as the differential mediating role of emotion regulation between moral injury and negative emotions, have not yet been systematically revealed.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to identify the latent subtypes of moral injury among medical staff via latent profile analysis and to explore the mediating role of emotion regulation and its core strategies between moral injury and negative emotions, so as to provide empirical evidence for formulating targeted psychological intervention strategies for medical staff.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional survey. 1001 medical staff were enrolled via a convenience sampling method. Questionnaires were used to assess moral injury, anxiety, depression and emotion regulation among medical staff, with latent profile analysis applied for subgroup identification. The relationships among variables were analyzed using independent-samples t-test, analysis of variance, Pearson correlation analysis, latent profile analysis and mediation analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThree profiles of moral injury were identified and designated as the Lowest (11.39%), Med-low (74.52%), and Highest (14.09%) groups. Significant associations were observed among emotion regulation, moral injury, and negative emotions. Emotion regulation exerted a significant mediating effect on the relationship between moral injury and depression, as well as anxiety; the two subscales of emotion regulation (Cognitive Reappraisal and Expressive Suppression) were also found to play similar mediating roles.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study offers recommendations for developing psychological interventions and management strategies to mitigate negative emotions in medical staff. Given that moral injury and emotion regulation significantly influence medical staff\u0026rsquo; negative emotions, administrators should account for individual differences in moral injury. For medical staff with high moral injury, priority should be given to guiding adaptive emotion regulation strategies, with an emphasis on cognitive reappraisal, to alleviate negative emotions and safeguard nursing care quality.\u003c/p\u003e","manuscriptTitle":"Heterogeneity of Moral Injury and Its Pathway to Negative Emotions in Medical Staff: The Differential Mediating Role of Emotion Regulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 07:50:32","doi":"10.21203/rs.3.rs-9334719/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T08:15:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T18:47:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T12:21:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T09:47:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285338023228534032818636628272457832934","date":"2026-04-26T06:35:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124788034360563394426818509079996604737","date":"2026-04-25T13:19:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135324818622901580363184906027349804736","date":"2026-04-24T09:06:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T10:21:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-07T07:41:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T00:16:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T00:16:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-04-06T14:05:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8bef24e0-db8c-44b3-adc4-cd09417cc17e","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T08:15:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T18:47:54+00:00","index":37,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T12:21:48+00:00","index":36,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T09:47:43+00:00","index":35,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T08:27:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 07:50:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9334719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9334719","identity":"rs-9334719","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}