EXPLORING THE POWER OF DOCUMENTARY FILM TO CHALLENGE DEMENTIA-RELATED STIGMA AND SUPPORT DANCE FOR LIFE ENRICHMENT
Bibliographic record
Abstract
Abstract Dance has been shown to support empowerment, sociability, and creative self-expression; however, it is rarely adopted with the explicit intention to support these aspects of engagement in the context of dementia care. Instead, dance is primarily adopted as a form of therapy to improve cognitive and physical health outcomes. This is far too narrow given there is research that demonstrates the power of dance to increase social inclusion by supporting embodied self-expression, creativity, and social engagement of people living with dementia. In order to foster broad community awareness building and education about the value of dance for life enrichment and the importance of supporting this in dementia care settings, we developed a short documentary film – Dancer Not Dementia. To evaluate the impact of the film, we conducted focus groups and/or interviews with people living with dementia, family carers, government policy makers, formal care providers affiliated with long-term and community-based dementia care, members of the dance community, and the general public. Data were collected within one week of watching the film and then again 8-12 weeks later. Our analysis illustrates the effectiveness of Dancer Not Dementia in reducing stigma by demonstrating changes in understandings of dementia and changes in practice. Our analysis also includes attention to how the film, as a form of cultural production, deepened engagement and facilitated transformation. This research contributes to a growing understanding of how the arts may challenge entrenched and oppressive attitudes and social relations, and support more inclusive and relational approaches to caring.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".