In recent years, the mental health challenges faced by healthcare professionals have garnered increased global attention, especially in high-stress environments such as hospitals and clinics. Among these professionals, nurses often find themselves on the front lines, experiencing not only the pressures of critical clinical decision-making but also the emotional toll that comes with adverse patient outcomes. A groundbreaking study led by Liu, Xiao, and Qu, published in BMC Psychology in 2025, delves deeply into this complex landscape, investigating how the phenomenon known as the “second victim experience” profoundly affects depressive symptoms among nurses in China. This research offers novel insights by employing a latent class analysis, a powerful statistical technique designed to identify hidden subgroups within populations, thereby enhancing our understanding of psychological empowerment and support systems in mitigating mental health crises.
The term “second victim” refers to healthcare providers who experience trauma following involvement in an adverse patient event, such as medical errors or unexpected complications. These individuals often suffer considerable emotional distress, complicating their professional and personal lives. The study conducted by Liu and colleagues illuminates the multifaceted nature of such experiences in nursing, emphasizing that the repercussions extend beyond immediate emotional reactions toward longer-term psychological degradation, such as depressive symptoms. Given the scale of China’s healthcare system and the vital role nurses play, quantifying and addressing these mental health challenges is imperative.
Employing latent class analysis allowed the researchers to categorize nurses into distinct psychological profiles based on their experiences with second victim phenomena, perceived support, and levels of psychological empowerment. This methodology transcends conventional analytical approaches by identifying hidden patterns and groupings within data, providing more nuanced perspectives than binary or linear analyses permit. The elucidation of these classes revealed critical heterogeneity in nurses’ responses to adverse events and highlighted the intricate interplays between support mechanisms and empowerment in buffering depressive symptoms.
Remarkably, the study finds that psychological empowerment—a psychological state wherein nurses perceive control over their work environment, experience competence, meaningfulness, and autonomy—is a significant protective factor against depression. Nurses with higher empowerment scores were less likely to show severe depressive symptoms, even when their exposure to second victim experiences was elevated. This finding proposes a transformative approach to mental health interventions, suggesting that fostering intrinsic empowerment may be as crucial as providing external support systems.
Another compelling aspect of the study is its comprehensive assessment of support interventions. Support, in this context, encompasses organizational backing, peer support, and professional counseling services. The research demonstrates a complex but essential link: adequate support not only mediates the direct emotional impact of traumatic clinical events but also bolsters psychological empowerment, creating a synergistic protective effect. This dual pathway underscores the importance of integrated support strategies that incorporate both structural and psychological dimensions.
Liu and his team underscore the cultural and systemic particularities of the Chinese healthcare context that influence these dynamics. For instance, high workload, hierarchical organizational structures, and stigmatization of mental health issues can exacerbate the vulnerability of nurses to depressive symptoms. The study suggests that culturally sensitive models of support and empowerment tailored to the unique socio-professional realities of Chinese nurses are vital. It advocates for policy reforms that embed mental health considerations into routine occupational health protocols.
Additionally, the application of latent class analysis in this setting is particularly innovative, as it captures diverse psychological trajectories that would otherwise be concealed in aggregate data. By identifying subpopulations ranging from highly resilient, supported, and empowered nurses to those at high risk due to insufficient support and psychological disempowerment, the study provides a roadmap for targeted interventions. These differentiated approaches promise more efficient allocation of resources and improve outcomes through personalized mental health care planning.
This research contributes to the burgeoning literature on healthcare worker well-being by linking the second victim concept with actionable psychological variables. It challenges institutions worldwide to rethink the traditional reactive models of addressing nurse burnout and depression, advocating for proactive psychological empowerment as a foundational pillar. The findings resonate well beyond China, offering transferable insights for global health systems seeking to safeguard their frontline workers in an era of increasing clinical complexity.
Furthermore, the study’s implications extend into ethical and operational domains. Enhancing psychological empowerment aligns with principles of professional respect and autonomy, critical for maintaining the morale and job satisfaction of nurses. Moreover, robust support systems signal an organizational commitment to employee welfare, which can reduce turnover rates and sustain high standards of patient care. Together, these pathways create a virtuous cycle of improved mental health and institutional effectiveness.
The researchers also highlight the potential for technological interventions to amplify support and empowerment, such as digital platforms delivering cognitive-behavioral therapy, peer support networks, and empowerment training modules. These innovations align with the increasing digitization of healthcare and may offer scalable, cost-effective solutions to the mental health crisis among nurses. Future research could explore these avenues to validate efficacy and implementation strategies.
In conclusion, Liu, Xiao, and Qu’s meticulous exploration of the second victim experience through latent class analysis unearths critical determinants of depressive symptoms among Chinese nurses. Their emphasis on the protective roles of psychological empowerment and support redefines mental health strategies, advocating for multi-level, culturally attuned, and evidence-based interventions. As healthcare systems worldwide grapple with workforce sustainability, this study provides both a scientific framework and an urgent call to action.
This breakthrough underscores the necessity of reimagining workplace mental health in healthcare settings—not merely as a response to crises but as an integral component of professional identity and clinical excellence. The strategic elevation of psychological empowerment combined with robust support structures promises a healthier, more resilient nursing workforce equipped to face the evolving challenges of modern medicine.
Ultimately, the study’s pioneering use of advanced analytical techniques to decode the mental health patterns among nurses sets a new standard in psychosocial occupational research. It invites policymakers, clinicians, and researchers to collaborate in designing environments that not only mitigate harm but actively cultivate psychological wellbeing. The health of nurses, as the study poignantly reveals, is inseparable from the health of the patients and communities they serve.
Subject of Research: The impact of second victim experiences, support, and psychological empowerment on depressive symptoms among nurses in China.
Article Title: Impact of second victim experience and support and psychological empowerment on depressive symptoms among nurses in China – a latent class analysis.
Article References:
Liu, H., Xiao, Y. & Qu, H. Impact of second victim experience and support and psychological empowerment on depressive symptoms among nurses in China – a latent class analysis. BMC Psychol 13, 951 (2025). https://doi.org/10.1186/s40359-025-03291-x
Image Credits: AI Generated
