Nurse Education Today最新文献

Background: The Australian nursing academic workforce is undergoing a significant demographic shift, characterised by accelerated retirement and departure of senior academics. This trend threatens continuity of mentorship, academic leadership, and institutional knowledge, with potential implications for early- and mid-career nurse academics.

Objectives: To explore nurse academics' lived experiences of senior mentorship loss and to examine perceived impacts on induction, workload, role clarity, succession planning, and resilience within a regional university context.

Design: A qualitative study informed by a hermeneutic phenomenological orientation, an interpretive approach concerned with understanding meaning derived from lived experience.

Methods: Three face-to-face focus groups were conducted with early- and mid-career nurse academics (3 groups; N = 15; n = 5 per group) at a regional Australian university. Data were analysed using reflexive thematic analysis following Braun and Clarke's six-phase analytic process to identify shared patterns of meaning across participants' accounts.

Results: Five themes were identified: (1) Loss of Institutional Knowledge and Mentorship; (2) Inadequate Induction and Orientation Processes; (3) Workload Intensification and Role Ambiguity Following Senior Academic Loss; (4) Organisational Gaps in Succession Planning and Communication; and (5) Strategies for Recovery, Retention, and Resilience. Participants described emotional and professional strain associated with mentorship voids, unclear expectations, increased invisible workload, and inconsistent organisational responses during workforce transition.

Conclusions: The findings underscore mentorship as a critical element of academic sustainability and highlight limitations in existing induction, workload, and knowledge-transfer systems during periods of senior workforce attrition. Strengthening formalised and integrated mentorship and succession-support mechanisms may assist nursing education leaders to enhance retention, professional identity, and workforce resilience.

Background: Graduate nursing education requires effective supervisory competence to prepare advanced practice nurses and nursing scientists. While personality traits and psychological capital influence supervisory effectiveness, the complex interrelationships among these psychological constructs and specific supervisory competencies remain inadequately understood through traditional analytical approaches.

Objective: This study examined the component-to-component network structure connecting Big Five personality traits, psychological capital, and nursing supervisory competence dimensions among graduate nursing supervisors, identifying key bridge nodes and providing systems-level understanding of psychological mechanisms underlying supervisory effectiveness.

Design: Cross-sectional network analysis study employing Gaussian Graphical Models.

Setting: Various Chinese universities offering nursing graduate programs and their affiliated clinical institutions across Eastern, Central, and Western China.

Participants: 294 nursing graduate supervisors were recruited through convenience sampling from July to September 2025.

Methods: Data collection utilized validated instruments measuring Big Five personality traits, psychological capital, and supervisory instructional abilities. Network analysis employed Gaussian Graphical Models with regularization techniques, centrality indices computation, and bridge expected influence analysis to identify critical connectors between supervisory domains.

Results: Academic guidance ability emerged as the node with highest expected influence, while hope demonstrated the strongest centrality within psychological capital. Conscientiousness exhibited the highest bridge expected influence across all constructs (0.532), with self-efficacy serving as the primary psychological capital bridge (0.363). Neuroticism displayed negative bridging effects (-0.343), indicating inhibitory influence on cross-domain connections. Network stability analysis confirmed robustness of centrality findings across different sample configurations.

Conclusions: The network structure reveals a multilevel psychological system where stable personality traits establish foundations for developable psychological capital, which subsequently connects to concrete supervisory competencies. These findings suggest that faculty development interventions targeting key network nodes and strengthening critical bridge connections may achieve superior outcomes compared to approaches focusing on individual variables independently.

Background: Research on nursing and midwifery students' experiences with end-of-life care in Neonatal Intensive Care Units (NICUs) is limited, indicating the need for deeper exploration. Understanding their experiences and educational needs may inform curricula, ensuring future healthcare professionals are better equipped for these complex and emotionally challenging situations.

Aim: To explore the experiences of nursing and midwifery students with end-of-life care in NICUs.

Design: A qualitative descriptive exploratory study using focus group interviews.

Settings: This study was conducted at the Erasmus Brussels University of Applied Sciences and Arts, Brussels, Belgium.

Participants: A total of 23 students, including 8 postgraduate nursing students, 5 undergraduate nursing students enrolled in a paediatric course module, and 10 midwifery students, participated in the focus groups.

Methods: Focus groups were recorded, transcribed verbatim, and analysed using thematic analysis. Ongoing discussions among the researchers were used to support reflexivity and investigator triangulation and to develop and refine the themes.

Results: Key themes identified included emotional challenges, feeling unprepared for end-of-life care realities, and the need for both emotional and professional support. Students reported difficulties in managing grief, forming bonds with families, and addressing ethical dilemmas in decision-making. A gap in education was evident, with students highlighting the need for more hands-on experience, particularly in communication competences and culturally sensitive care.

Conclusions: This study underscores the need for enhanced preparation and support for nursing and midwifery students in NICUs. Integrating end-of-life care competencies, mentorship, and interprofessional simulation-based learning into curricula may help support students to handle the emotional and ethical complexities of end-of-life care. Strengthening emotional support and collaboration may further contribute to students' capacity to provide compassionate care for families and to work effectively within healthcare teams.

The persistent "what works" question in nurse education research conceals a conceptual trap. We seek universal interventions that reliably produce outcomes regardless of context, building an evidence base where promising interventions repeatedly fail to transfer across settings. Critical realism and its methodological offspring, realist evaluation, offers a transformative alternative from the social sciences. Rather than asking "does this work," realist evaluation asks: for whom, in what circumstances, through what mechanisms, producing which outcomes? This Big Ideas paper introduces the Context Mechanism Outcome (CMO) configuration framework as analytical architecture for understanding how educational interventions operate. Using Resilience Based Clinical Supervision as an exemplar, I demonstrate how realist approaches generate middle range theories - conditional, contextualised knowledge about what tends to work for whom in what circumstances. Nurse education's characteristic features (diverse student populations, constitutive context, complex interventions, multiple stakeholders) make realist approaches essential rather than optional. The transformation required is epistemological: abandoning false universalism to build cumulative knowledge about how mechanisms operate under varying conditions. This produces an evidence base adequate to nursing education's complexity, offering practitioners sophisticated conceptual resources for contextually intelligent implementation rather than simplistic prescriptions.

Nursing and midwifery students manage large volumes of course content and varying levels of importance, yet many feel overwhelmed. To support prioritization, a Traffic Light System (TLS) was introduced in three nursing and midwifery courses at an Australian university. The TLS classifies content as Green (supplementary), Amber (important), and Red (critical), guiding students toward high-impact learning areas.

Aim: This study evaluated the TLS's effectiveness in reducing cognitive load, improving time management, and supporting academic performance.

Methods: A case study design examined the TLS implementation in an online neonatal care course and two blended Human Pathophysiology and Pharmacology courses. Quantitative data were analysed using descriptive statistics and effect size (Cohen's h) to compare positive agreement across cohorts. Qualitative feedback was thematically coded to identify key perceptions of TLS usefulness. The TLS was introduced in Care of the Neonate in 2018 and in the P&P courses in 2023. Data were collected across 13 institutional survey cycles (2018-2025) using Likert-scale and open-ended responses. Cumulative feedback included 146 neonatal and 192 P&P students. Analyses highlight the first neonatal course year (2018) and the most recent P&P cycle (2025).

Results: In 2018, 85.6% of neonatal students (n = 83) rated TLS positively, with 62.7% (n = 52) strongly agreeing it supported learning. In 2025 P&P data (n = 129), 82.2% rated the TLS positively, with 56.6% (n = 73) strongly agreeing. The difference between cohorts showed a small, stable effect size (h = 0.09), confirming consistency across time and courses. Qualitative feedback revealed five themes: clearer study prioritization, improved focus, ease of use, deeper engagement, and greater satisfaction.

Conclusions: The TLS enhances curriculum clarity, time management, and cognitive load reduction. Integration of quantitative and qualitative outcomes shows the TLS is statistically consistent and valued by students, supporting targeted, high-impact learning across diverse courses.

The rapid integration of generative artificial intelligence (GenAI) into undergraduate nursing education has prompted significant debate regarding its impact on the development of critical reasoning, inquiry skills, and clinical judgement. While some scholars argue that reliance on GenAI may undermine independent thinking, contextual decision‑making, and autonomous judgement, emerging perspectives suggest that GenAI has the potential to enhance rather than erode these foundational competencies. This commentary examines the evolving role of GenAI in nursing education and argues that its thoughtful integration can strengthen students' preparedness for increasingly complex, technology‑rich clinical environments. Clinical judgement is central to safe nursing practice and is shaped by the nurse's interpretation of patient needs, contextual factors, and professional reasoning. While GenAI can synthesize large amounts of information efficiently, it does not replace human judgement; instead, it provides data that students must interpret within ethical, relational, and contextual dimensions of care. Integrating GenAI into educational contexts allows students to engage with realistic, data‑driven scenarios that mirror contemporary practice environments, supporting deeper analytical thinking and the ability to critique algorithmic outputs and biases. At the same time, the use of GenAI raises epistemological tensions between nursing's humanistic ways of knowing and AI's computational logic. These tensions underscore concerns that tacit knowledge, ethical reasoning, and patient‑centered judgement may be marginalized if GenAI tools are used uncritically. Addressing this challenge requires adapting nursing theory and curriculum to incorporate digital epistemologies while maintaining the profession's ethical and relational foundations. This commentary concludes that rather than discouraging GenAI use, nursing education must embrace it deliberately and ethically. Through intentional curriculum design, faculty development, and emphasis on AI literacy, educators can ensure that nursing students emerge as competent, reflective practitioners capable of navigating GenAI‑enabled healthcare environments with confidence and integrity.