JMIR Nursing

Midwifery education in Nigeria is undergoing a transformation in pedagogical approach. Existing traditional teaching materials are being supplemented by digital technology-enhanced learning resources; however, educators’ limited pedagogical capacity may hinder their adoption. Thus, train-the-trainer (TTT) workshops were implemented to build capacity among midwifery educators to prepare them for the implementation of a digital learning platform across 20 institutions in Nigeria.

AI is increasingly being integrated into education and health care, offering opportunities to improve learning, understanding of clinical cases, and students’ self-confidence. However, it remains necessary to assess nursing students’ perceptions of AI and its impact on their academic and professional development.

Proxy-rated questionnaires remain the standard for the assessment of activity and sleep for people with dementia living in nursing homes. Sensing technologies, such as wearables, can generate continuous data that provide quantitative insights into daily activities and behavioral and psychological symptoms, such as sleep disturbance. This study explores the use of sensing technologies in the detection of changes in physical activity levels and sleep behaviors over time.

Monitoring of vital signs is a critical component of nursing care, facilitating the early detection of complications, enabling the observation of the progression of health status, and providing a foundation for diagnosis and therapeutic decision-making. It is instrumental in ensuring quality of care. Contactless measurement methods, such as radar-based sensors, have the potential to enable continuous monitoring of respiration, heart rate, and bed activity without disturbing or stressing vulnerable patients.

Nursing records are a critical reflection of nursing practices and play a key role in ensuring the quality of patient care. However, these records sometimes fail to accurately represent the real-world health truth, resulting in information distortion. The manifestations and potential consequences of such distortion remain understudied, which may hinder efforts to improve nursing practices.

Poor self-care impairs osteoporosis management. Disease-related knowledge and family support have influenced self-care; however, the combined association of these factors with self-care remains insufficiently understood.

Shift work disorder and insufficient sleep are prevalent among nurses, leading to fatigue, reduced well-being, and potential safety concerns. The increasing use of wearable sleep-tracking devices presents an opportunity to evaluate nurses’ sleep quality objectively.

Enhancing nursing students’ awareness, attitudes, beliefs, and preparedness toward AI may help improve their health care knowledge and practice.

Health care professionals are exposed to high levels of occupational stress, emotional exhaustion, and anxiety-depressive symptoms. Digital interventions using mindfulness- and acceptance-based approaches have shown promising results in improving psychological well-being in this population. The MINDxYOU program is a self-guided digital intervention designed to reduce perceived stress and enhance emotional regulation, resilience, and psychological flexibility. Although its effectiveness has been demonstrated, treatment response may vary substantially between individuals.

Patient safety investigation reports support organizational learning only when they are complete, usable, and sufficiently detailed. Conventional free-text reports are often inconsistent and may omit information needed for review and learning. Project NARRATE (Nursing AI-Refined for Accurate Transcription of Events) is a nursing-led ambient artificial intelligence workflow that uses prompts aligned with the World Health Organization Minimal Information Model for Patient Safety Incident Reporting and Learning Systems, Situation-Background-Assessment-Recommendation output, and visible missing-information cues to support structured supervisor reporting.

Assistive robots have been proposed to support nursing work by offloading routine, logistical, and physically demanding tasks. However, existing research has largely focused on postimplementation acceptance or usability, with fewer studies examining how nurses conceptualize assistive robots before routine deployment, particularly in settings where exposure remains limited.

Clinical AI decision support is being introduced into nursing practice; however, existing large language models (LLMs) demonstrate only moderate accuracy on complex clinical tasks, raising questions about the level of accuracy required for safe clinical use across varying levels of clinician experience and task complexity.