Nurse Retention

New nurses often find it difficult to adjust when they start working in fast-paced, high-pressure trauma units, and many leave their jobs within the first two years. This study looks at whether a two-day training programme, called Basic Trauma Training (BTT), helps newly graduated nurses in Saudi trauma hospitals feel more confident, satisfied and engaged in their work, and more likely to stay in the nursing profession. The study takes place at two trauma hospitals in Saudi Arabia. Because it is not practical to randomly assign nurses to groups in this real-world hospital setting, the study compares an earlier group of new nurses, who receive standard hospital orientation only, with a later group, who receive standard orientation plus the two-day BTT training. Both groups complete questionnaires before and after their orientation or training period, answering questions about job satisfaction, engagement, stress and their plans to stay in the role. The study also looks at actual staff retention using hospital employment records at 6 and 12 months. Nurses in the comparison group are offered the BTT training later, once their part of the study is complete, so that everyone has the chance to take part in the training.

Newly graduated nurses often experience high levels of psychological stress, sleep disturbance, fatigue, and burnout during the early transition into clinical practice. Early identification of burnout and retention risk may help improve mental well-being, workforce stability, and quality of patient care. This longitudinal observational study aims to develop a non-invasive sleep-based prediction platform for assessing burnout and retention risk among postgraduate nurses. Participants will undergo repeated psychological assessments and non-contact sleep monitoring during the study period. Sleep-related physiological parameters, including sleep efficiency, sleep structure, heart rate variability, and respiratory variability, will be collected together with validated psychological questionnaires. The study will further apply machine learning and artificial intelligence approaches to integrate longitudinal physiological and psychological data for risk prediction and early identification of burnout-related conditions. The findings may support future development of precision mental health monitoring and supportive management strategies for high-stress healthcare workers.