Educational escape rooms for healthcare students: A systematic review
Bibliographic record
Abstract
OBJECTIVES: To synthesize evidence on the implementation and evaluation of educational escape rooms for healthcare students. DESIGN: A systematic review. DATA SOURCES: A search of seven databases including PubMed, Scopus, Web of Science, Cochrane, EMBASE, CINAHL, and Education Resources Information Center was conducted for published studies from inception to January 2023. The selected articles were assessed by two independent researchers for meeting the inclusion criteria and methodological quality. The data was narratively synthesised. RESULTS: Fifty-two studies were included in the review. Escape rooms were employed as learning and assessment strategies for the delivery of diverse topics within dentistry, medical, nursing, pharmacy and physiotherapy pre-registration healthcare education. Most were conducted on nursing students with learning topics focusing on the development of knowledge related to patient care management. Virtual escape rooms became more prevalent following the COVID-19 pandemic. The specific design features of escape rooms were based on the elements of game-based learning including team learning with appropriate size, time limit and sequential path-design of puzzles. Applying the experiential learning approach, the facilitators played a pivotal role in pre-briefing, facilitating the gameplay, and debriefing. Escape rooms were evaluated to be enjoyable and have an impact on students' cognitive, psychomotor, non-technical and affective skills learning outcomes. However, the learning outcomes compared with conventional learning strategies were inconclusive due to lack of randomised controlled trials. CONCLUSION: Escape rooms are increasingly being used as experiential and game-based learning in healthcare education. It will continue to evolve with virtual technology advancement and is a promising tool to provide virtual team training within interprofessional education for the development of teamwork and communication skills. While more rigorous research using a randomised controlled trial design is needed to evaluate the effectiveness of escape rooms and their design features, qualitative studies, particularly on the perspective of facilitators are needed to unravel the learning process.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".