To map the digital ethics studies published to date in the field of nursing.
The integration of digitalization in the healthcare sector, especially in nursing care, is increasing. However, digitalization in nursing can raise professional, personal, and ethical concerns among nurses. A thorough discussion of digital ethics in nursing is still lacking in the literature.
A scoping review was registered in the Open Science Framework and reported here according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews guidelines. The PubMed, CINAHL, and Scopus databases were searched. The reference lists of included studies were also screened. The results were summarized and discussed using the patterns, advances, gaps, evidence, and recommendations methodology.
A total of 394 studies were identified, of which seven met the inclusion criteria and were included. Digital ethics have been investigated in the nursing field around three main themes: (i) knowledge and awareness of digital ethics; (ii) digital ethical violations in nursing care; and (iii) digital ethical violations regarding ethical principles.
Overall, the concept of digital ethics in nursing is still in its early stages. It should be further developed and harmonized to help researchers search the literature and clinicians find available evidence. Additionally, the patterns identified in studies can serve as the basis for a digital ethics framework, which should be further refined.
Digital technologies present several potential ethical implications at the (a) individual levels, affecting nurses and patients, and (b) collective levels, impacting all nurses, health systems, and citizens. Increasing awareness and knowledge, and developing guidelines specific to nursing, should be a priority to prevent and minimize risks. Codes of conduct should address these emerging issues and promote the ethical use of digital tools. Audits and monitoring systems should be established to detect additional risks and address them appropriately.
This study aimed to explore and clarify the concept of professional engagement among clinical nurses.
Nurses are central to shaping healthcare systems through direct interactions with patients and communities. Despite their potential to influence health policy, their involvement in policy development remains limited, especially in culturally specific contexts like Iran. Understanding the nature of professional engagement is essential for strengthening nursing practice and informing policy.
This qualitative descriptive study employed a conventional content analysis approach. Data were collected through semi-structured interviews with 16 clinical nurses selected via purposive sampling to ensure diversity in hospital type, ward specialty, and geographic location across Iran.
The analysis yielded four main categories: benevolence, vigor, professional emotional belonging, and meaningful care. They reflect the humanistic, energetic, emotionally connected, and purpose-driven nature of nursing practice.
Professional engagement in nursing is shaped by intrinsic motivation, emotional connection, and a strong sense of purpose. These factors contribute to nurses’ commitment and the quality of care they provide.
Professional engagement in nursing is defined by intrinsic satisfaction in helping others, emotional attachment to the profession, and a strong sense of purpose. These are features that distinctly set nursing apart from other professions.
Supporting nurses’ emotional connection, teamwork, and sense of purpose can strengthen professional engagement. Nurse managers should foster reflective practice and meaningful caregiving to improve work motivation and care quality.
Policymakers should implement strategies that reinforce engagement across workshifts. Policy-driven efforts can enhance patient safety, care quality, and nurse retention at various caring levels.
To explore the nature and content of Canadian media coverage surrounding the COVID-19 pandemic by wave – spanning from 2020 to 2024, identifying health policy and system responses that emerged as strategies in retaining the nursing workforce.
COVID-19 was an unprecedented health crisis impacting healthcare delivery and the healthcare workforce globally. It caused tremendous turbulence and strain, testing system capacity due to staff shortages, resource limitations and issues accommodating high volumes and acuity levels of patients. Across Canada, the pandemic resulted in mobilization of policy and system responses to address workforce challenges that emerged.
A qualitative content analysis was conducted of online media coverage from nursing organizations and government websites published from January 2020 to March 2024, as well as newspaper articles from the Canadian Newsstream database.
Synthesized findings were categorized according to waves of the pandemic, highlighting key themes surrounding system and policy responses that emerged. Government investments to increase wages, educate additional staff and enhance protection for workers were among the measures employed.
This review captures the evolution and progression of the health crisis, its impact on the nursing workforce and associated responses. Workforce development and retention were emphasized through measures to enhance mental health and wellness, improve protection for workers and address safe staffing practices.
Nurses played a pivotal role throughout the global pandemic. Multiple system and policy responses were identified as key facets in strengthening, supporting and sustaining the nursing workforce.
Measures implemented illustrate the instrumental role that stakeholders, including the government and nursing organizations, play in building capacity and prioritizing efforts to protect nurses and enhance preparedness for future outbreaks.
This study examined the relationship between belief in free will (BFW) and job performance among nurses during the COVID-19 pandemic, with a particular focus on the mediating roles of grit and positive affect in this relationship.
Job performance is a critical aspect of nursing management, influencing patient care, nurses’ career trajectories, and organizational effectiveness, especially during pandemics like COVID-19. Understanding the individual-level psychosocial factors contributing to nurses’ job performance is therefore essential.
This study used a cross-sectional, descriptive survey design and adhered to the STROBE reporting guidelines. From January 2021 to May 2022, 709 Chinese nurses in Chengdu and Kunming completed a web-based questionnaire survey that included validated measures of BFW, grit, positive affect, job performance, and Big Five personality traits. Descriptive statistics, bivariate correlations, and common method bias analyses were first conducted using IBM SPSS 25.0 software. Multiple mediation analyses were then conducted using the SPSS PROCESS macro.
We found a moderate to large positive relationship between nurses’ BFW and their job performance (r = 0.44, p < 0.001). Furthermore, this relationship was mediated by both grit and positive affect, with a significant serial mediation effect observed (p < 0.05 for all). Importantly, our findings remained significant after controlling for Big Five personality traits.
Our study reveals a robust link between BFW and job performance in nurses and identifies grit and positive affect as key mediators of this relationship.
Nursing leaders can improve their nurses’ job performance by cultivating BFW and developing a supportive and empowering work environment to increase nurses’ grit and positive affect.
To evaluate the capability of large language models to generate nursing diagnoses based on NANDA-I Taxonomy II and assess their performance across domains and overall.
Large language models are emerging tools in nursing, showing potential to aid in diagnosis generation and education. However, their accuracy and applicability in clinical and educational settings remain underexplored.
This cross-sectional comparative study used 10 realistic patient scenarios based on NANDA-I Taxonomy II, covering 12 domains. The study aimed to evaluate the capability of four models to generate nursing diagnoses based on patient scenarios. The responses were assessed by five nursing experts for accuracy and alignment with NANDA-I Taxonomy II in a single-blind evaluation process.
All models demonstrated similar performance across different domains and overall, with Claude attaining the highest overall performance score. Expert evaluations indicated moderate interrater reliability.
Small variations between models and occasional omissions suggest that expert review is still required before clinical use.
Large language models are not yet sufficiently reliable for independent use in clinical settings and nursing education. Their application as supportive tools necessitates a cautious approach. Moreover, the development of specialized models designed to address the unique demands of the nursing field would be advantageous.
When large language models are used in nursing practice, their limitations should be considered, and the outputs they produce should be verified by nurses.
Ensuring the safe integration of artificial intelligence tools into nursing necessitates the establishment of robust regulatory policies to safeguard patient safety, the deployment of effective systems to monitor models’ performance, and the development of comprehensive guidelines and training programs.
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