Technology‐Enabled Dementia Care Innovations: Vaccine Education Exergame for People with Dementia
Bibliographic record
Abstract
BACKGROUND: Vaccines significantly reduce the risk of various illnesses, such as Influenza, Pneumonia, and COVID-19, all of which are among Canada's top ten causes of death. However, vaccine hesitancy remains a concern, especially among higher-risk populations, including older adults and persons with dementia (PWD). Enhancing current vaccine education may aid in addressing vaccine hesitancy. OBJECTIVES: This mixed-methods pilot study aims to explore the physical, social, and educational benefits of using educational exergaming to enhance vaccine education among persons with dementia (PWD) and their caregivers. METHODS: The development of the Bingo-style vaccine education exergame was informed by a scoping review examining current digital interventions for persons with dementia (PWD) and older adults, highlighting the need for interactive and cognitively inclusive digital tools. A pilot study was then conducted with PWD and caregivers using a bingo-style vaccine educational exergame design. Data were collected through post-intervention surveys and semi-structured interviews. Quantitative data were analyzed descriptively, while qualitative data was analyzed thematically. RESULTS: Preliminary findings reveal positive attitudes towards the educational exergame, emphasizing the increase of vaccine knowledge and confidence. Furthermore, the exergame was found to be highly usable. However, challenges were noted in the complexity of vaccine education content and the length of the program. CONCLUSION: The study revealed that the bingo vaccine educational exergame is highly usable with physical, social, and educational benefits for persons with dementia and their caregivers. However, further research is needed to understand how varying cognitive impairments may shape user experience and design preferences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".