ADVANCES IN GERONTOLOGY CURRICULUM DESIGN: CENTRING LIVED EXPERIENCES OF LGBTQ+ OLDER ADULTS
Bibliographic record
Abstract
Abstract While research on LGBTQ+ aging in Canada has increased, post-secondary educators have identified gaps in pedagogical aids to facilitate their teaching in this area. Older LGBTQ+ individuals have unique social/historical contexts compared to their majority peers, often involving minority stress experiences (e.g., stigma, discrimination, physical violence) that contribute to inequalities during late life. To create a future workforce that understands and appreciates the unique needs and social/historical contexts of aging LGBTQ+ individuals, our team prioritized developing curriculum resources that center the voices of LGBTQ+ older adults. We interviewed 26 LGBTQ+ older adults (M = 65.9, Range = 51-89). Participants shared stories about their own life courses and how they reflected the landscape of current curricula. They offered strategies for enhancing curriculum to ensure that post-secondary education include diverse LGBTQ+ narratives. Through conventional content analysis we identified topics that LGBTQ+ older adults named as requirements for students to understand the experiences of LGBTQ+ aging within hetero- and cis-normative health/social contexts. Through our analysis we articulated the importance of co-construction of educational materials with LGBTQ+ communities. Participants also identified the development of tools to be used within classrooms to challenge instructors to unpack their own assumptions about LGBTQ+ aging. They shared the importance of highlighting unique experiences of aging within these communities including the histories of queer/trans discrimination and the experiences of survivors of the HIV/AIDs crisis. Findings from this project will inform curriculum design and resources that move beyond normativity and include narratives of the lived/living experiences of LGBTQ+ older adults.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".