GAMIFICATION OF DEMENTIA EDUCATION IN ACUTE CARE
Bibliographic record
Abstract
Background: About 40% of older people in general hospitals have dementia, and evidence showed that hospitalization has detrimental effects on people with dementia. Hospital leaders are challenged to engage staff in change of attitudes, knowledge and culture to meet the changing needs of patient population. This poster presents a project of using gamification to achieve the goal of motivating staff engagement and passion to improve care of patients with dementia. Methods: We used qualitative methods to investigate the benefits and challenges of using gamification in staff education for dementia care. Gamification refers to applying game thinking to non-game context to make learning more exciting, fun and effective. Our intervention focused on game dynamics such as receiving rewards, recognition, social experience and appreciation. Staff received surprise prizes when they completed various games in two fun fairs. These included virtual badges, recognition from clinical leaders and points redeemable for prizes. The fun fairs were videotaped, and the video data were co-analyzed by staff and the researchers in focus groups. Thematic analysis was conducted. Results: The results of the education intervention included three themes: (a) games reinforced previous learned knowledge in dementia care; (b) healthy competitions among staff created fun learning experiences; (c) collective learning inspired commitment to actions. Conclusion: Our findings suggest that gamification could tap into both extrinsic and intrinsic motivations and has potential to increase sustained staff behavioural change.
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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 | 0.004 | 0.009 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".