Educational games created by medical students in a cultural safety training game jam: a qualitative descriptive study
Bibliographic record
Abstract
Abstract Background Cultural safety training, whereby health professionals learn to reflect on their own culture and to respect the cultural identity of patients, could address intercultural tensions in health care. Given the context of their medical education, however, medical students might perceive such training to be dull or even unnecessary. Game jams, collaborative workshops to create and play games, are a potentially engaging learning environment for medical students today. How medical students learn while making games is poorly documented. This study describes the characteristics of educational games created by participants in a cultural safety game jam and the concepts they used to create games. Methods As part of a trial, 268 Colombian medical students divided into 48 groups participated in an eight-hour game jam to create a prototype of an educational game on cultural safety. In this qualitative descriptive study, we reviewed the description of the games uploaded by participants, including the name, objective, game narrative, rules, rewards, penalties, and pictures. An inductive thematic analysis collated their descriptions. Results The game descriptions illustrated the characteristics of the educational games and the aspects of the cultural safety concept that the students used to create games. Medical students situated cultural safety within a continuum with culturally unsafe actions at one end and cultural safety at the other end. Although not familiar with game design, the students designed prototypes of basic educational games including game dynamics, game scenarios, learning objectives, and pedagogical strategies. Conclusion The findings of this study could help researchers and educators to understand how medical students learn from game design and the kind of games that game jam participants can create without previous game design skills.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".