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Creating a healthy work environment requires balancing organizational goals with ethical responsibilities, where head nurses' ethical leadership can shape staff outcomes by mitigating work–family conflicts and promoting nurses' well-being, retention, and patient safety. This study aims to analyze the mediating role of work–family between head nurses' ethical leadership and nurses' reported errors, turnover intention, and physical and mental health.
Nationwide Multicenter cross-sectional study.
Validated self-report scales were used to assess nurses' perceptions of head nurses' ethical leadership, work–family conflict, error, turnover intention, physical and mental health. Descriptive and inferential analyses were conducted. Structural equation modeling examined the relationships among these variables based on Della Bella's and Fiorini's framework.
Data from 409 nurses across seven Italian hospitals was analyzed. The structural equation model showed an excellent fit. Head nurses' Ethical leadership was negatively associated with work–family conflicts, turnover intention, and errors, and positively associated with nurses' health. Work–family conflicts were significantly linked to turnover intention, errors, and nurses' health. Work–family conflicts mediate the relation between ethical leadership and turnover intention, errors, and nurses' health.
Promoting healthy work environments is crucial for nurses', patients', and organizations' well-being. Ethical leadership helps achieve this condition by reducing work–family conflicts, fostering nurses' well-being, decreasing turnover intention, and improving care quality. Disseminating ethical leadership programs and integrating with work–life balance policies can therefore strengthen both staff retention and organizational outcomes.
Ethical leadership can foster patient care, reduce turnover intention and errors, and improve nurses' well-being. Therefore, maintaining employee performance and organizational results requires integrating work–life balance policies with ethical leadership development programs.
The study adhered to The Strengthening the Reporting of Observational Studies in Epidemiology checklist.
This study did not include patient or public involvement.
The study was preregistered on the Open Science Framework https://osf.io/8jk37/overview.
This study did not include patient or public involvement in its design, conduct, or reporting.
To predict nurses' turnover intention using machine learning techniques and identify the most influential psychosocial, organisational and demographic predictors across three countries.
A cross-sectional, multinational survey design.
Data were collected from 1625 nurses in the United States, Türkiye and Malta between June and September 2023 via an online survey. Twenty variables were assessed, including job satisfaction, psychological safety, depression, presenteeism, person-group fit and work engagement. Turnover intention was transformed into a binary variable using unsupervised machine learning (k-means clustering). Six supervised algorithms—logistic regression, random forest, XGBoost, decision tree, support vector machine and artificial neural networks—were employed. Model performance was evaluated using accuracy, precision, recall, F1 score and Area Under the Curve (AUC). Feature importance was examined using logistic regression (coefficients), XGBoost (gain) and random forest (mean decrease accuracy).
Logistic regression achieved the best predictive performance (accuracy = 0.829, f1 = 0.851, AUC = 0.890) followed closely by support vector machine (polynomial kernel) (accuracy = 0.805, f1 0.830, AUC = 0.864) and random forest (accuracy = 0.791, f1 = 0.820, AUC = 0.859). In the feature importance analysis, job satisfaction consistently emerged as the most influential predictor across all models. Other key predictors identified in the logistic regression model included country (USA), work experience (6–10 years), depression and psychological safety. XGBoost and random forest additionally emphasised the roles of work engagement, group-level authenticity and person–group fit. Job-stress-related presenteeism was uniquely significant in XGBoost, while depression ranked among the top predictors in both logistic regression and random forest models.
Machine learning can effectively predict turnover intention using multidimensional predictors. This methodology can support data-driven decision-making in clinical retention strategies.
This study provides a data-driven framework to identify nurses at risk of turnover. By integrating machine learning into workforce planning, healthcare leaders can develop targeted, evidence-based strategies to enhance retention and improve organisational stability.
This study adhered to STROBE reporting guideline.
This study did not include patient or public involvement in its design, conduct or reporting.
Surgical oncology patients often experience doubts and uncertainties in the preoperative and postoperative periods, which can be addressed remotely through telenursing. Expanding telenursing services could contribute to more comprehensive perioperative care. We conducted a scoping review to characterise these telenursing services, identify their outcome indicators and examine the content of the care delivered.
A scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) recommendations.
MEDLINE (PubMed), EMBASE, CINAHL, SCOPUS, Web of Science and Virtual Health Library (VHL), with searches performed up to 5 May 2025.
We included studies that implemented telenursing interventions in the preoperative or postoperative period in adult oncology patients.
Two independent reviewers used a standardised search to select and extract data from the included studies. Study characteristics were presented descriptively using absolute and relative frequencies, and the content of telenursing interventions was organised into a circular thematic matrix.
A total of 37 studies were included, published between 1996 and 2024, conducted in 12 countries and primarily focused on postoperative telenursing via telephone or video calls. Preoperative care focused on psychosocial support and guidance related to surgical preparation. Postoperative topics included surgical wound care; handling of devices such as drains, ostomy bags and catheters; instructions for returning to work and support groups for financial and social assistance. Outcome indicators were primarily related to care, including levels of anxiety, stress, depression and quality of life.
Oncologic surgical telenursing remains primarily focused on postoperative care and the delivery of personalised support. Reporting on the protocols used, frequency and duration of sessions, nurse training and profiles, integration with in-person care workflows and operational cost data could strengthen the knowledge base for perioperative telenursing in oncology.
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