Using Podcasts as a Learning Tool to Decrease Stigma and Increase Awareness of the Experience of Dementia
Bibliographic record
Abstract
Abstract Dementia Dialogue is a podcast produced by the Alzheimer Society of Ontario that provides people with lived experience (people living with dementia and care partners) a way to share their stories with each other and the broader community. Listeners who have dementia and care partners gain insight and strengthen their adaptive skills. Listeners who work with people living with dementia and care partners as well as community members at large, develop an understanding of what it means to live with dementia and how these individuals can be supported, thereby increasing awareness about the experience of dementia and decreasing stigma. A series of Learning Guides utilizing these podcasts have been created to help people with lived experience of dementia as well as others within the community, specifically professionals and volunteers supporting those with dementia and their care partners, to explore the various themes of the Dementia Journey. The Learning Guides describe key points along the dementia journey and use embedded podcast excerpts that demonstrate these themes. Excerpts are followed by reflection/discussion questions targeted to specific learner groups to aid formulation of ideas about what can be learned and how it can be applied in practice to one’s own experience. Learners can engage with the guides through independent self‐study or in group settings with a facilitator. Focus groups and surveys were conducted with people with lived experience, professionals/volunteers and educators to assess utility of the learning guides and explore the potential for development of elearning versions of the learning guides. Delegates will learn about the podcast, learning guides and results from the testing of these.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".