Game-Based Learning Interventions to Foster Cross-Cultural Care Training: A Scoping Review
Bibliographic record
Abstract
Objective: Differences in cultural background between health providers and patients can reduce effective access to health services in multicultural settings. Health sciences educators have recently suggested that game-based learning may be effective for cross-cultural care training. This scoping review maps published knowledge on educational games intended to foster cross-cultural care training and highlights the research gaps for future research. Materials and Methods: A scoping review searched PubMed, Eric, Embase, Lilacs, PsycINFO, and Google Scholar for theoretical and empirical research, using terms relevant to cross-cultural care and game-based learning. A participatory research framework engaged senior medical students and participatory research experts in conducting and evaluating the review. Results: Forty-one documents met the inclusion criteria, all from developed countries. The most common source of publication was nursing and medicine (39%; 16/41) and used the cultural competence approach (44%; 18/41). Around one-half of the publications (51%; 21/41) were theoretical and 39% (16/41) were empirical. Empirical studies most commonly used mixed methods (44%; 7/16), followed by strictly quantitative (31%; 5/16) or qualitative (25%; 4/16) approaches. There were no randomized controlled trials and only one study engaged end-users in the design. Empirical studies most frequently assessed role-play-related games (44%; 7/16) and used game evaluation-related outcomes or learning-related outcomes. None used patient-oriented outcomes. Findings suggest that educational games are an effective and engaging educational intervention for cross-cultural care training. Conclusions: The paucity of studies on educational games and cross-cultural care training precludes a systematic review. Future empirical studies should focus on randomized counterfactual designs and patient-related outcomes. We encourage involving end-users in developing content for educational games.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".