Design and Development of an Escape Game as a Knowledge Transfer Tool in Preparation for an Accreditation Visit in a Health Care Facility
Bibliographic record
Abstract
Background: Knowledge transfer helps health care staff to be competent, well informed, and up to date. It also contributes to adherence to standards and best practices. Objectives: To design, implement, and evaluate an escape game based on a selection of Accreditation Canada required organizational practices (ROPs). Methods: This prospective descriptive study involved nurses and pharmacists in a health care centre. An escape game based on 6 ROPs was designed. The game was played by teams of participants in a patient room within the centre, with each game lasting 25 minutes. Participants' satisfaction with various aspects of their experience was assessed. Results: = 28) completed the game within the allotted time (average completion time 20 minutes, 53 seconds; standard deviation [SD] 2 minutes, 45 seconds). On average, 1.32 (SD 0.88) clues were provided to successful teams and 1.88 (SD 0.95) to unsuccessful teams. Participants were very satisfied with their experience. However, members of unsuccessful teams had significantly lower agreement that the escape game was relevant to their practice and that it was an effective method of communication. Conclusions: An escape game based on a selection of ROPs was successfully implemented as part of the hospital's preparation for an accreditation visit. Use of an escape game as a knowledge transfer tool was appreciated by the staff.
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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 | 0.006 |
| 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 | 0.002 | 0.002 |
| 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".