The power of story: Bringing 2SLGBTQ+ digital stories into gerontology settings
Bibliographic record
Abstract
Two-spirit, lesbian, gay, bisexual, trans, and queer or questioning (2SLGBTQ+) older adults are underrepresented in gerontology research and education, impacting the preparedness of health and social care students and professionals working with the diverse aging population. To address this lack of representation of 2SLGBTQ+ older adults in gerontology education, this study explored the use of digital stories as tools for knowledge mobilization and social justice. Digital stories are short videos that pair audio recordings with visuals, including videos, photographs, or artwork. To conduct the study, the research team worked alongside 2SLGBTQ+ older adults to create a suite of three digital stories. These stories were presented at various educational and professional settings in gerontology, and survey and open-feedback responses (n = 147) were gathered from the audience on their perceived impact. Viewers included students, researchers, decision-makers, stakeholders, and citizens. Content analysis was used to analyze the data. From the analysis, digital stories showed the potential to increase viewers' awareness and understanding of 2SLGBTQ+ aging experiences. The format was particularly significant in their learning and enhancing the connection to the content and the storytellers. The findings also suggest that digital stories showed the potential to impact policy and practice for 2SLGBTQ+ 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".