Game-Based Learning Interventions to Foster Cross-Cultural Care Training: A Scoping Review
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it