Acceptability study of a co-designed educational game about dementia for children: The Kids Dementia Game
Bibliographic record
Abstract
BACKGROUND: Dementia has physical, psychological, social and economic impacts, not only for people living with dementia, but also for their carers, families and wider society. Due to the growing number of people living with dementia, children are increasingly likely to encounter family members living with dementia. The aim of this project was to pilot an educational game which was co-designed with children and people living with dementia with the intention of improving children's understanding and perception of dementia. RESEARCH DESIGN AND METHODS: An acceptability study of the Kids Dementia Game was conducted in three classes in three schools in Northern Ireland. This study investigated acceptability of the game and the feasibility of online data collection using a pre-post test methodology to explore how best to collect evaluation data if the game was to be delivered on a larger scale. RESULTS: Evaluation of the game with children showed a positive level of acceptability of the game. Children found the game engaging, easy to navigate and fun to play. Feasibility of the data collection method was found to be a barrier to the pre-post test evaluation of the game. DISCUSSION AND IMPLICATIONS: These findings suggest that the game shows evidence of promise for improving public perception and understanding of dementia using an early intervention approach with children.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".