Objective
This intersectional analysis was designed to explore how lesbian, bisexual, and queer (LBQ) women of color understand and navigate family formation decisions.
Background
Family formation research centers White heterosexual parents and heteronormative pathways (i.e., adoption and cryobank purchased sperm). Choosing a known donor may be a way for LBQ women of color to circumvent a process that has not been responsive to their needs.
Method
Our qualitative analysis of 13 interviews of LBQ parents in families of color examined (a) the processes through which queer women of color arrive at the selection of a known donor, (b) the characteristics that queer women of color prioritize in donor selection, and (c) how women's interactions with external institutions (e.g., cryobanks) and histories of oppressive racialized family formation practices influence their decision‐making.
Results
Participants arrived at the selection of known donors because the desired donor characteristics were unavailable through commercial sperm banks, particularly with regard to the intersection of a person who could be known and mirrored specific racial, ethnic, and cultural characteristics. This decision was highly connected to their individual identities and the intersections of those identities.
Conclusion
LBQ women of color may choose known sperm donors and seek to minimize their use of biotechnology because they do not consider other alternatives (e.g., bank‐acquired sperm) desirable or feasible.
Implications
Findings invite the reimagination of a cryobanking system that operates on a relational rather than biomedical model and the need for services that practice outside of White, heteronormative paradigms.
Prior research documents associations between personal network characteristics and health, but establishing causation has been a long-standing research priority. To evaluate approaches to causal inference in egocentric network data, this article uses three waves from the University of California Berkeley Social Networks Study (N = 1,159) to investigate connections between nine network variables and two global health outcomes. We compare three modeling strategies: cross-sectional ordinary least squares regression, regression with lagged dependent variables (LDVs), and hybrid fixed and random effects models. Results suggest that cross-sectional and LDV models may overestimate the causal effects of networks on health because hybrid models show that network–health associations operate primarily between individuals, as opposed to network changes causing within-individual changes in health. These findings demonstrate uses of panel data that may advance scholarship on networks and health and suggest that causal effects of network support on health may be more limited than previously thought.
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