An innovative gamification tool to enhance intercultural competence and self-efficacy among healthcare professionals caring for vulnerable migrants and refugees
Bibliographic record
Abstract
BACKGROUND: The growing number of vulnerable migrants and refugees (VMRs) in the European Union presents challenges to healthcare systems, emphasizing the need for enhanced intercultural competence training for healthcare professionals. Educational escape rooms, using gamification-based principles, may offer an innovative solution to improve these competencies. OBJECTIVE: This pilot study evaluates the acceptability and preliminary effectiveness of an educational escape room aimed at improving intercultural competence, self-efficacy, and knowledge among healthcare students and professionals caring for VMRs. METHODS: A pre-post, single-group pilot study was conducted with 101 healthcare students and professionals, recruited through convenience sampling. Participants engaged in an educational escape room simulating a migratory crisis, designed to foster collaborative problem-solving under pressure. A newly validated questionnaire was administered before and after the intervention to measure changes in intercultural competence, self-efficacy, and knowledge. Paired t-tests were used to analyze pre-post differences, and thematic analysis explored participant feedback on the learning experience and the acceptability of the intervention. RESULTS: Significant improvements were observed in intercultural competence (d = 1.13, p < 0.001), self-efficacy (d = 0.38, p = 0.001), and knowledge (d = 1.19, p < 0.001). Participants reported high engagement, satisfaction, and an enhanced understanding of healthcare challenges related to VMRs. The escape room was deemed acceptable. CONCLUSIONS: This pilot study provides evidence of the acceptability and effectiveness of an educational escape room in enhancing intercultural competence, self-efficacy, and knowledge. Further research with larger, more rigorous studies is recommended to confirm these findings and explore scalability.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".