SafeHome: A Serious Game to Promote Safe Environments for Persons Living with Dementia
Bibliographic record
Abstract
The dementia epidemic continues to affect families across Canada. The number of persons living with dementia (PLWD) is projected to reach 1.1 million over the next 20 years, placing further financial and resource constraints on the Canadian healthcare system. Caregiver education is vital in ensuring the quality of life and safety for PLWD and can increase the time they are able to live at home, which is correlated with positive outcomes for both PLWD and their caregivers, and a reduction in system costs. However, current educational support often requires individuals to travel to local, urban service care centers and educational content is often provided in English, which can exacerbate the difficulties faced by marginalized caregivers (e.g., immigrants and those living in rural settings) who are caring for PLWD. To address this issue, a team of researchers developed a serious game called "SafeHome" that teaches safety strategies by having players identify and rectify potential hazards in the home setting that may negatively impact on PLWD outcomes, such as falls. A usability study was conducted using an adapted, validated questionnaire and semi-structured focus groups to better understand users' experience and obtain suggestions for the SafeHome serious game improvement. Results indicated that 80% of the participants were satisfied with the activities provided through SafeHome. All participants (n = 13) made recommendations for improving the usability, functionality, and comprehensiveness of the educational content. This feedback will inform future iterations of SafeHome and add valuable contributions to the growing literature on innovative e-learning resources that support PLWD and their caregivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".