MOBILIZE RAPID RESEARCH KNOWLEDGE UPTAKE BY USING GAMIFICATION
Bibliographic record
Abstract
The topic of what motivates staff to accept new research knowledge and apply in practice is an important one to explore. This project involves using gamification (game thinking and mechanics) to support rapid knowledge uptake. The traditional academic publication offers limited effectiveness as practitioners often find the content boring and difficult to retain. This project aimed to increase engagement, accessibility, knowledge, and effectiveness of research knowledge uptake in dementia care among hospital staff. We took a participatory action approach to engage staff to co-design an online game, called the ART & SCIENCE of Person-Centred Care for learning 10 basic care techniques identified in a dementia research led by the first author. A total of 70 staff members (nurses, physicians, occupational therapist, physiotherapist, and unit clerk) in the medical and mental health programs of Vancouver General Hospital were involved in testing the games by using multiple action cycles. The project was evaluated by a knowledge test and a survey of staff experience. Over 200 staff played the online game to learn dementia care techniques. Staff reported that they not only gained knowledge and skills in caring for patients with dementia but also had fun and enjoyed the competition. The social experience associated with the game stimulated ongoing engagement and active learning among people in the hospital. In this presentation, we will demonstrate what we have learned about the impact of applying gamification in knowledge translation. Impacts related to improvements in knowledge and engagement will be discussed.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".