Factors Influencing Attrition and Retention of Nursing Staff in India: A Review - Ms. Shruti Tiwari and Prof. (Dr.) Rajendra Singh (International Journal of Advanced Research in Science Communication and Technology, 2026)
Nursing staff form the backbone of every healthcare delivery system, and their stability is directly linked to the quality, safety, and continuity of patient care. In India, hospitals and healthcare organisations increasingly report high rates of nursing attrition, which strain already limited human resources and inflate the costs associated with recruitment, orientation, and training. This review synthesises the existing literature on the factors that influence attrition and retention among nursing staff in India, drawing on both domestic studies and comparable international evidence. The available research indicates that attrition is rarely driven by a single cause; rather, it emerges from the interaction of individual, organisational, economic, and socio-cultural factors. Low remuneration, heavy workloads, limited career progression, weak organisational support, and migration opportunities abroad have repeatedly been identified as key drivers of turnover Conversely, retention is strengthened by supportive leadership, competitive compensation, a positive organisational culture, structured career development, and meaningful employee engagement. This review is organised around a general introduction to the problem context, an examination of the healthcare workforce environment, a discussion of methodological approaches used in the field, and an integrated literature review covering demographic and personal factors, organisational and workplace factors, and economic and career-related factors. Three summary tables consolidate the findings across studies. The review concludes that a holistic, evidence-based retention strategy—combining fair pay, humane working conditions, and clear professional growth pathways—is essential to stabilise the nursing workforce in India. Directions for future research, including the need for longitudinal and predictive modelling studies, are also outlined.