Understanding the Processes of Role Adoption in Pedagogical Role-Play Games (RPGs): A Narrative Dramaturgical Perspective on “Social RPGs” in Health Professions Education
Bibliographic record
Abstract
Background Health professional training interventions based on role-playing games (RPGs) have been shown to be an increasingly popular way to advance health professional students’ skills in communication and empathetic engagement with patients and colleagues. However, role adoption itself is largely assumed, with little research focusing on how students come to engage, or fail to engage, in role play. Objective This study aimed to identify the processes by which healthcare professionals and trainees (HCP/Ts) adopt roles in role-based serious games designed for health professions education (HPE). The theory of narrative dramaturgy informed this qualitative study, to illuminate the relationship between the participant and the role. Methods Four focus groups were conducted at the conclusion of four iterations of an RPG, in which different groups of healthcare professionals participated, focused on joint deliberation over a case in pediatric oncology. The data were analyzed thematically. Results & Conclusion Four themes were developed that characterize the process of role adoption: role commitment; simultaneous evocation of front and back stages; reflexivity; and visceral lingering. Our findings contribute to delineating the processes of role adoption that suggest specific conditions under which role play may or may not be beneficial, and how it can be taught and enhanced in health professional education. In doing so, the study draws attention to an under-researched form of RPG – a “social RPG” – grounded in interaction.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".