A Holistic Clustering Methodology for Liver Transplantation Survival

Using Clustering Methods to Map the Experience Profiles of Dementia Caregivers

Background and Objectives

Caregivers of persons living with dementia report wide-ranging lived experiences, including feelings of burden and frustration but also positivity about caregiving. This study applies clustering methodology to novel survey data to explore variation in caregiving experience profiles, which could then be used to design and target caregiver interventions aimed at improving caregiver well-being.

Research Design and Methods

The k-means clustering algorithm partitioned a sample of 81 caregivers from the Midwest region of the United States on the basis of 8 variables capturing caregiver emotions, attitudes, knowledge, and network perceptions (adversity: burden, anxiety, network malfeasance; network nonfeasance; positivity: positive aspects of caregiving, preparedness and confidence in community-based care, knowledge about community services for older adults, and network uplift). The experience profile of each segment is described qualitatively and then regression methods were used to examine the association between (a) experience profiles and caregiver demographic characteristics and (b) experience profiles and study attrition.

Results

The clustering algorithm identified 4 segments of caregivers with distinct experience profiles: Thriving (low adversity, high positivity); Struggling with Network (high network malfeasance); Intensely Struggling (high adversity, low positivity); Detached (unprepared, disconnected, but not anxious). Experience profiles were associated with significantly different demographic profiles and attrition rates.

Discussion and Implications

How caregivers respond to support interventions may be contingent on caregivers' experience profile. Research and practice should focus on identifying public health strategies tailored to fit caregiver experiences.

Clinical Trial Registration

NCT03932812.

Evaluation of neighborhood resources and mental health in American military Veterans using geographic information systems

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Four meaningful neighborhood resource groups were identified by clustering.

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Living in alcohol-permissive/tobacco-restrictive neighborhoods had negative impacts.

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Place of residence and distance to the closest VA care facility were not significant.

Neighborhood-level social determinants are increasingly recognized as factors shaping mental health in adults. Data-driven informatics methods and geographic information systems (GIS) offer innovative approaches for quantifying neighborhood attributes and studying their influence on mental health. Guided by a modification of Andersen’s Behavioral Model of Health Service Use framework, this cross-sectional study examined associations of neighborhood resource groups with psychological distress and depressive symptoms in 1,528 U.S. Veterans. Data came from the Veteran Affairs (VA) Health Services Research and Development Proactive Mental Health trial and publicly available sources. Hierarchical clustering based on the proportions of neighborhood resources within walkable distance was used to identify neighborhood resource groups and generalized estimating equations analyzed the association of identified neighborhood resource groups with mental health outcomes. Few resources were found in walkable areas except alcohol and/or tobacco outlets. In clustering analysis, four meaningful neighborhood groups were identified characterized by alcohol and tobacco outlets. Living in an alcohol-permissive and tobacco-restrictive neighborhood was associated with increased psychological distress but not depressive symptoms. Living in urban or rural areas and access to VA care facilities were not associated with either outcome. These findings can be used in developing community-based mental health-promoting interventions and public health policies such as zoning policies to regulate alcohol outlets in neighborhoods. Augmenting community-based services with Veteran-specialized services in neighborhoods where Veterans live provides opportunities for improving their mental health.

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative

Aim

To develop a consensus paper on the central points of an international invitational think‐tank on nursing and artificial intelligence (AI).

Methods

We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3‐day invitational think tank in autumn 2019. Activities included a pre‐event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities.

Implications for nursing

Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice.

Conclusion

There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems.

Impact

We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.