Effective nursing leadership is critical for healthcare organizations’ sustainability as nurse leaders influence many organizational aspects, including staff retention, quality of care, resource management, and development. The aim of this study is to highlight the quality achievements and benefits that transformational leadership may have for healthcare organizations. An extensive literature search has been conducted through MEDLINE and Scopus. The 6-stage framework pro- posed by Arksey and O’Malley was applied, guided the scoping review process. Data extracted from the included studies were systematically charted. This approach allowed for a comprehensive understanding of the advantages of transformational leadership in healthcare organizations. Of the 1245 searched articles, 26 encountered the study’s inclusion criteria. Analysis of the studies led to the formulation of two thematic categories, namely, (a) transformational leadership and human resources and (b) transformational leadership and healthcare delivery. Results indicated that transformational nursing leadership can benefit healthcare organizations in terms of effective resource management, high quality of care, and sustainability. Visionary leaders support staff retention, innovation, and research and promote organizational status and development. Through continuous support and training, transformational nurse leaders can ensure nurses’ job satisfaction and engagement, patients’ satisfaction, the best therapeutic outcomes, and high levels of organizational achievement.

Background/Objectives: Toxic leadership has arisen as a matter of serious concern within the nursing profession, with growing evidence linking it to diminished job satisfaction, ineffective conflict management, and weakened organizational commitment. These effects not only compromise nurse retention but also threaten the quality of patient care and overall healthcare outcomes. This scoping review aimed to examine the impact of toxic nursing leadership on staff retention by synthesizing evidence from existing literature and a broad range of published studies. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed/MEDLINE, Scopus, CINAHL and Science Direct databases yielding 1356 articles. Of these, 18 met the predefined inclusion criteria. The scoping review followed the six-stage methodological framework proposed by Arksey and O’Malley. Thematic analysis identified two core categories: (a) key dimensions shaping perceptions of toxic leadership and (b) the impact of toxic leadership on nursing staff retention. Results: The findings reveal that toxic leadership contributes to organizational silence, emotional exhaustion, diminished psychological safety, and low professional commitment. Such behaviors not only jeopardize nurse engagement and productivity but also negatively affect patient safety and care quality. In contrast, leadership styles such as transformational and transactional leadership are associated with higher job satisfaction, reduced burnout, and improved retention outcomes. Conclusions: This review underscores the need for healthcare organizations to identify and address toxic leadership behaviors promptly. By promoting supportive and ethical leadership styles, institutions can foster a healthier workplace, improve nurse retention, and ultimately enhance the quality of care. The study offers practical implications for healthcare administrators, emphasizing leadership development.

Effective nurse leadership increases nurses’ job satisfaction, performance, motivation, and empowerment. Ensuring nurses’ organizational and professional commitment is vital for addressing quality, sustainability, and cost-effectiveness challenges in a high-demanding healthcare environment. The aim of this study was to explore the impact of transformational leadership on nurses’ organizational and professional commitment. A systematic review was conducted using the Scopus, ScienceDirect, PubMed/Medline, Scilit.net, and Wiley Online Library databases. Original research studies published between 2016 and 2024 focusing exclusively on nurses’ professional and organizational commitment and transformational leadership were included. The indexing terms “nursing leadership”, “transformational leadership”, “professional/organizational commitment”, and “nursing” were used along with the Boolean operators AND, OR, and NOT. According to the findings, transformational leadership practices increase nurses’ organizational and professional commitment through vision sharing, motivation, career development, interpersonal communication, a positive working environment, and productive resources management. The healthcare policy makers should invest in organizing, supporting, and promoting relational leadership practices to retain their nursing workforce and obtain their organizational and professional commitment to ensure high care quality.

Background/Objectives: Toxic leadership has arisen as a matter of serious concern within the nursing profession, with growing evidence linking it to diminished job satisfaction, ineffective conflict management, and weakened organizational commitment. These ef-fects not only compromise nurse retention but also threaten the quality of patient care and overall healthcare outcomes. This scoping review aims to examine the impact of toxic nursing leadership on staff retention by synthesizing evidence from existing literature and a broad range of published studies. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed/MEDLINE, Scopus, CINAHL and Science Direct databases yielding 1,356 articles. Of these, 18 met the predefined inclusion criteria. The scoping review followed the six-stage methodological framework proposed by Arksey and O’Malley. Thematic analysis identified two core categories: (a) key dimensions shaping perceptions of toxic leadership and (b) the impact of toxic leadership on nursing staff retention. Results: The findings reveal that toxic leadership contributes to organizational silence, emotional exhaustion, diminished psychological safety, and low professional commitment. Such behaviors not only jeopardize nurse engagement and productivity but also negatively affect patient safety and care quality. In contrast, leadership styles such as transformational and transactional leadership are as-sociated with higher job satisfaction, reduced burnout, and improved retention outcomes. Conclusions: This review underscores the need for healthcare organizations to identify and address toxic leadership behaviors promptly. By promoting supportive and ethical leadership styles, institutions can foster a healthier workplace, improve nurse retention, and ultimately enhance the quality of care. The study offers practical implications for healthcare administrators, emphasizing leadership development.

Background/Objectives: Healthcare service quality is a critical dimension of patient safety and operational performance. Traditional assessment approaches are often limited in their ability to support prediction, which has increased interest in machine learning (ML) models. This systematic review assessed the effectiveness of ML algorithms in predicting healthcare service quality metrics, with emphasis on their applications, comparative performance and implementation challenges. Methods: A systematic literature search was conducted in PubMed/MEDLINE, Scopus, CINAHL, and ScienceDirect for studies published between 1 January 2020 and 30 July 2025. Following title/abstract screening and full-text review, 49 studies were included. Studies were grouped into conventional/ensemble ML and deep learning categories based on the primary model class analyzed in each article. Results: The reviewed studies focused mainly on acute clinical and operational outcomes, especially length of stay (27.0%), mortality (23.0%), and readmission rates (18.0%), while subjective, patient-centered metrics received less attention. Conventional and ensemble ML models, particularly RF and XGBoost, were frequently reported, while deep learning models were used in more complex prediction tasks. Conclusions: The evidence suggests that well-validated and interpretable ML models can support healthcare quality prediction. However, important challenges remain regarding implementation, validation, generalizability, and data heterogeneity.