Impact of EMBODY Experiencing Dementia through New Media Exhibit: Engaging nursing students and the public about dementia through arts-based knowledge translation methods
Bibliographic record
Abstract
Objective: This paper aims to look at the impact of arts-based knowledge translation (ABKT) methods in raising awareness among nursing students and the public about the sex differences and behavioral and psychological symptoms of dementia (BPSD), implemented through a creative new media arts-exhibit at Toronto Rehabilitation Institute.Methods: Through surveys, interviews, and pre- and post-exhibit questionnaires, this project evaluated the use and efficacy of multi-modal media in translating data from a study on the BPSD. The research team categorized and conceptualized artwork and narratives based on data collected from previous phases; no confidential or identifying information related to study participants were used or displayed in the final exhibit.Results: The use of photographs, paintings, abstract data visualizations, augmented reality and virtual reality had various levels of effectiveness in engaging nursing students and the public on the topic of dementia. 360º videos, photographs, and paintings provided the highest level of engagement and discussion among nursing students. The majority of the students reported a better understanding and empathy towards people living with dementia after the viewing of the exhibit and all students perceived the exhibit as an effective method in portraying dementia experience and contributed to their overall understanding of the BPSD.Conclusions: This knowledge mobilization project overall provided a more informed perspective on BPSD among nursing students and the public, and effectively sparked discussion among viewers. The exhibit was able to raise awareness for dementia, its symptoms, and experiences of patients living with dementia. For the scholarly community, our project presents new ways to mobilize knowledge among a broad audience and demonstrates unique, innovative, and engaging forms of ABKT.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".