EQUITABLE AND SOCIALLY JUST AGE-FRIENDLY COMMUNITIES FOR OLDER LGBTQ+ ADULTS OF COLOR
Bibliographic record
Abstract
Abstract The age-friendly movement has been critiqued for inadequately addressing the needs of historically disadvantaged populations, like older LGBTQ+ adults of color. This presentation describes a multi-year participatory action research project with five older Black lesbian women aged between 65 and 75. We hosted five focus groups where participants shared their daily experiences living in an age-friendly city and proposed ideas to more inclusively meet the needs of older LGBTQ+ adults of color within age-friendly activities. Focus group data were analyzed thematically, and four themes were identified which expand existing age-friendly frameworks, promoting greater equity and justice: 1) attending to the interaction between identities and structures throughout the life course, 2) understanding the impact of historical and contemporary experiences of violence and discrimination on health and aging, 3) addressing the evolving sociopolitical landscape regarding the human rights and freedoms of diverse older adults, and 4) developing interventions that create safe and affirming spaces for older LGBTQ+ adults of color. While mainstream age-friendly frameworks are widely acknowledged for enhancing the health, independence, and social connectivity of older adults, addressing the unique challenges associated with aging while navigating multiple intersecting marginalized identities requires a critical perspective. The findings of this research underscore the necessity of culturally tailored age-friendly frameworks and interventions to address the distinct needs and experiences of older LGBTQ+ adults of color. We argue that gerontology must seriously consider the impact of privilege and oppression on the health and aging experiences of vulnerable and underserved populations within age-friendly 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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".