BRINGING DIVERSE STORIES OF AGING INTO THE CLASSROOM: A FOCUS ON LGBTQ+ AGING
Bibliographic record
Abstract
Despite population aging, gaps in curricula and training related to gerontology are well documented across disciplines. Even within programs and courses that include content on aging, little is focused on diverse experiences of aging. Older adults who are part of the LGBTQ+ community have unique social and historical contexts as well as health and social needs related to their sexual minority status. Yet, these factors are rarely embedded into training and education. In an effort to close this gap and to bring diverse stories of aging into the classroom 3 older adults who are members of the LGBTQ+ community created digital stories highlighting their aging experience. Digital storytelling is a process that allows individuals to create 2 to 3 minute-long videos that pair audio recordings of personal narratives with visuals. Paired with research on LGBTQ+ aging, these digital stories were presented to 175 students at three universities in Ontario, Canada in a range of disciplines (social work, health sciences, psychology, counseling, sociology, etc.). Students reflected on their experience viewing the stories and in particular how the stories impacted their understanding of LGBTQ+ aging. Responses were analyzed using conventional content analysis. Results indicated that students had powerful reactions to viewing these stories and students recognized their own assumptions, biases and lack of knowledge about LGBTQ+ aging. This study offers important pedagogical contributions to the field of gerontology. Embedding diverse stories of aging into curricula and training programs is vital for building capacity and expertise among future health and social care providers.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".