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To provide an early-stage integrative synthesis of shared and scenario-specific ethical risks of Conversational Artificial Intelligence in nursing triage and patient education, and to synthesize governance directions and limitations discussed in the current literature.
A systematic integrative review following the Whittemore–Knafl framework and PRISMA guidelines.
Eight databases were searched. Two researchers independently conducted screening, data extraction, and thematic coding, followed by inductive synthesis. Quality appraisal used design-appropriate tools according to article type. Ethical risks were analysed within and across scenarios. Registered in PROSPERO (CRD420251079144).
Nine articles were included (four on nursing triage; five on patient education), comprising two empirical studies, four reviews, two randomized controlled trial protocols, and one debate paper. Both scenarios shared six common ethical challenges: data privacy and security, over-reliance and deskilling, training-data bias and stigma reproduction, lack of empathy and emotional interaction capability, algorithmic black box and insufficient interpretability, and blurred accountability and regulatory gaps. Nursing triage presented additional risks including assessment inaccuracy, contextual misunderstanding, lack of personalization, superficially plausible misguidance and insufficient clinician trust. Patient education revealed four distinct issues: misleading information, digital accessibility gaps, cross-cultural and multilingual adaptation, and fairness and health inequality. Six shared governance directions were synthesized—human oversight and manual review, improvement of legal policies and industry standards, enhanced transparency and interpretability, continuous algorithm optimization and scientific validation, the human–machine balance principle, and capacity building for healthcare professionals. The literature also suggested scenario-specific reinforcements for triage and education.
Ethical risks of Conversational Artificial Intelligence in nursing show both common and scenario-dependent patterns. Given the limited and heterogeneous evidence base, the identified governance directions should be viewed as preliminary pathways requiring further validation.
This review offers evidence-informed ethical insights and scenario-based governance references to support safe, equitable and human-centred application of Conversational Artificial Intelligence in nursing practice.
Not applicable.
This study aims to explore occupational burnout among Chinese nurses from two perspectives: first, by comparing changes in emotional exhaustion, depersonalisation and personal accomplishment before and after the COVID-19 pandemic; and second, by identifying long-term work-related stressors and structural factors contributing to burnout.
A mixed-methods approach was adopted, combining a systematic review with qualitative interviews. The qualitative component involved semi-structured interviews with 53 hospital-employed nurses from various departments and regions across China, focusing on the three core dimensions of occupational burnout.
The systematic review included both Chinese and English-language studies published between 2016 and 2023 that used the Maslach Burnout Inventory to assess burnout among nurses. A total of 22 studies met the inclusion criteria, selected independently by two researchers using the JBI critical appraisal tool. In parallel, the qualitative interviews explored nurses' subjective experiences and coping strategies related to work stress, emotional fatigue and professional identity.
Bayesian factor analysis indicated no significant differences in emotional exhaustion (BF01 = 2.202), depersonalisation (BF01 = 2.761) or personal accomplishment (BF01 = 2.747) before and after the pandemic. Qualitative findings revealed that burnout was primarily driven by long-standing systemic stressors, including promotion pressure, clinical workload, organisational demands and work–family conflict. Although many nurses relied on self-regulation strategies to maintain psychological stability, they continued to experience ongoing physical and emotional exhaustion. Some reported emotional numbness, but most retained empathy and a strong sense of responsibility. Their sense of personal accomplishment often stemmed from patient recovery and recognition of professional value.
Occupational burnout among Chinese nurses remained largely stable before and after the COVID-19 pandemic. Its root causes stem from persistent work-related stressors and systemic issues, rather than the pandemic itself. Effective mitigation requires institutional strategies, including better staffing, clear career pathways and sustained emotional support.
Short-term crisis responses alone are insufficient to address enduring burnout. Nursing leadership should prioritise systemic reforms—such as optimising shift schedules, defining promotion channels and integrating regular psychological support—to enhance nurse well-being and care quality.
No patient or public contribution.
To identify predictors of nurses' perceived care quality, explore their understanding of high-quality care and propose improvement strategies to inform clinical practice.
A mixed-methods design, integrating quantitative data analysis and qualitative in-depth individual interviews.
Quantitative analysis used cross-sectional data from the 2017 Chinese Nursing Work Environment Survey (C-NWES). Chi-square tests and logistic regression were used to examine how demographic characteristics, work environment and occupational burnout predicted perceptions of care quality at hospital and unit levels. Qualitatively, 42 frontline nurses were interviewed in 2024 to explore their perceptions of care quality, predicting factors and improvement strategies in a post-pandemic context. Thematic analysis was applied to code and synthesise the interview data.
Quantitative analysis revealed that gender, education, workload, experience, work environment and burnout had differing impacts on nurses' care quality perceptions at hospital and unit levels. In-depth individual interviews revealed that nurses perceive high-quality care as patient-centred, predicted by factors such as human resources, occupational burnout, patient and family cooperation at the unit level and environmental and policies factors at the hospital level. Unit-level strategies included improving communication, team collaboration and leadership support, while hospital-level recommendations focused on welfare benefits, continuing education, flexible scheduling and resource optimisation. Through the mutual validation of quantitative analysis and in-depth interviews, this study revealed the multidimensional understanding and key predictors of care quality among frontline clinical nurses in China.
Work environment, occupational burnout and demographic factors significantly impact nurses' perceived care quality, highlighting the need for targeted organisational improvements at both unit and hospital levels to enhance care quality.
The findings highlight the importance of organisational interventions. Nursing managers should promote a positive work environment and mitigate burnout. Future research should develop testing models to explore the relationship between work environment and perceived care quality and validate their effectiveness.
No patient or public contribution.
To examine how gender differences in the nursing work environment shape nurses' perceived quality of care and to identify gender-specific predictors and evaluative mechanisms.
A mixed-methods design was employed, integrating quantitative data analysis with qualitative in-depth individual interviews.
This study was conducted in two phases: The first phase was a quantitative analysis, based on a large national dataset from the 2017 Chinese Nursing Work Environment Survey (N = 16,382), in which secondary analysis was performed using hierarchical linear regression, relative importance analysis, and network analysis to identify key predictors. The second phase was a qualitative study, in which in-depth individual interviews were conducted with 30 clinical nurses (15 male and 15 female), and thematic analysis was applied to explore gender-differentiated experiences.
The core finding of this study is that gender-differentiated factors within the work environment significantly shape nurses' perception of care quality. Quantitative results showed that the strongest predictor for female nurses was professional development, whereas recognition of value was most salient for male nurses. Qualitative results corroborated these findings: female nurses emphasised continuing education and emotional support, while male nurses emphasised fair evaluation and professional identity. Both groups reported that high-intensity workloads hindered the delivery of ideal humanistic care, inducing moral distress and emotional suppression and exposing ethical gaps in organisational support.
Gender differences in the nursing work environment shape pathways to perceived care quality and expose deeper managerial and ethical challenges. A gender-sensitive, ethics-oriented management approach can enhance nurse satisfaction and care quality, providing empirical support for optimising workforce allocation and sustaining healthcare systems.
Findings direct nurse leaders to tailor improvement strategies—enhancing professional-development infrastructure for women and strengthening recognition mechanisms for men—while embedding explicit ethical support to reduce moral distress and improve both workforce well-being and patient outcomes.
No patient or public contribution.
This study compares the emotional expressions and structural characteristics of workplace violence (WPV) against Chinese nurses in social media comments and news reports, highlighting differences and focal points in dissemination.
A quantitative study utilising text mining and social network analysis.
Data containing the keywords ‘nurse violence’, ‘nurse workplace violence’, ‘nurse bullying’, ‘nurse workplace bullying’ were collected from social media platforms (e.g., Xiaohongshu, Zhihu, Weibo) and news platforms (e.g., Baidu News, People's Daily, Xinhua News) between January 1, 2016, and October 31, 2024. Using Python 3.8.9, time trends and sentiment analyses were performed, while Ucinet 6.0 was used for social network analysis to explore dissemination patterns and keyword structures. A total of 5431 social media comments and 89 news reports were analysed.
Temporal analysis showed that social media attention to WPV against nurses significantly exceeded that of news reports, with a peak in 2024. Sentiment analysis revealed predominantly negative emotions (52.75%) on social media, while news reports exhibited a more positive tone (62.92%). Social network analysis revealed stark differences in keyword structures between platforms. Social media exhibited a dense and decentralised network, with keywords like ‘head nurse’, ‘leader’ and ‘bullying’ highlighting internal professional conflicts. In contrast, news reports showed a centralised network focusing on external violent incidents, with keywords such as ‘violence’, ‘assault’ and ‘patient’ dominating.
Social media and news reports demonstrated significant differences in describing WPV against nurses. Social media focused on emotional expressions of interpersonal conflicts, whereas news reports prioritised factual accounts of violent incidents and proposed solutions.
This study offers insights into how WPV against nurses is communicated through different media, helping nursing administrators and policymakers understand the complexity of these narratives. The findings can inform the development of targeted communication strategies to address WPV and enhance public awareness.
Not applicable.
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