Complex identities, intersectionality and research approaches in millennial family caregivers in the United States

Aims: A discussion of the personal and social contexts for Millennial family caregivers and the value of including complex identity and intersectionality in Millennial family caregiving research with practical application.

Design: Discussion paper.

Data sources: This discussion paper is based on our own experiences and supported by literature and theory.

Implications for nursing: Millennial family caregivers have distinct generational, historical and developmental experiences that contribute to the care they provide as well as their own well-being. Complex identity, the integration of multiple identities, and intersectionality, systems and structures that disempower and oppress individuals with multiple identities, need to be addressed in nursing research so intervention tailoring and health equity can be better supported in this population. From research conceptualization and design to data analysis, data must be used intentionally to promote equity and reduce bias. The inclusion of diverse Millennial caregivers throughout all stages of the research process and having a diverse nursing research workforce will support these efforts.

Conclusion: Millennial family caregivers comprise one-quarter of the family caregiving population in the United States, and they are more diverse than previous family caregiving generational cohorts. Their needs will be more fully supported by nursing scientists with the adoption of methods and techniques that address complex identity and intersectionality.

Impact: Nursing researchers can use the following research approaches to address complex identity and intersectionality in Millennial caregivers: inclusion of qualitative demographic data collection (participants can self-describe); data disaggregation; data visualization techniques to augment or replace frequencies and descriptive statistics for demographic reporting; use of researcher reflexivity throughout the research process; advanced statistical modelling techniques that can handle complex demographic data and test for interactions and differential effects of health outcomes; and qualitative approaches such as phenomenology that centre the stories and experiences of individuals within the population of interest.