INTERSECTING IDENTITIES AND HEALTH-RELATED QUALITY OF LIFE AMONG LGBTQ+ OLDER ADULTS
Bibliographic record
Abstract
Abstract LGBTQ+ older adults remain understudied in terms of the intersectionality of their identities and its impact on health. It is unclear how certain aspects of identity are perceived as central and how the intersection of multiple identities influences experiences of discrimination. This study identifies intersectional profiles of identity centrality and attributions for day-to-day discrimination, focusing on gender, sexual and gender identity, age, and race/ethnicity among LGBTQ+ older adults. It also examines the associations between profile membership, key psychological and social factors, and physical and mental health-related quality of life (HRQOL). Utilizing data from the National Health, Aging, and Sexuality/Gender Study, the first national longitudinal study of 2,450 LGBTQ+ older adults, we unveiled five latent profiles using Latent Class Analysis, which depict heterogeneous patterns in individuals’ perception of their identities in terms of centrality and as the basis of their discrimination experiences. The most prevalent was the moderate centrality and discrimination profile (C1; 31%), followed by high centrality and discrimination (C2; 26%), low centrality and discrimination (C3; 17%), high centrality and low discrimination (C4; 15%), and low centrality and high discrimination (C5; 11%). C2 and C5 exhibited lower physical and mental HRQOL compared to other profiles, suggesting that multiple discrimination attributes may contribute to compromised health, regardless of identity centrality. C2 and C4 showed high identity centrality across all identities, which was linked to greater critical awareness and activism. This study highlights the significance of incorporating an intersectional lens to better understand the distinct needs and strengths within LGBTQ+ aging communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".