Impact of game jam learning about cultural safety in Colombian medical education: a randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Cultural safety, whereby health professionals respect and promote the cultural identity of patients, could reduce intercultural tensions that hinder patient access to effective health services in Colombia. Game jams are participatory events to create educational games, a potentially engaging learning environment for Millennial medical students. We set out to determine whether medical student participation in a game jam on cultural safety is more effective than more conventional education in changing self-reported intended patient-oriented behavior and confidence in transcultural skills. METHODS: We conducted a parallel-group, two-arm randomized controlled trial with 1:1 allocation. Colombian medical students and medical interns at University of La Sabana participated in the trial. The intervention was a game jam to create an educational game on cultural safety, and the reference was a standard lesson plus an interactive workshop on cultural safety. Both sessions lasted eight hours. Stratified randomization allocated the participants to the intervention and control groups, with masked allocation until commencement. RESULTS: 531 students completed the baseline survey, 347 completed the survey immediately after the intervention, and 336 completed the survey after 6 months. After the intervention, game jam participants did not have better intentions of culturally safe behaviour than did participants in the reference group (difference in means: 0.08 95% CI - 0.05 to 0.23); both groups had an improvement in this outcome. Multivariate analysis adjusted by clusters confirmed that game jam learning was associated with higher transcultural self-efficacy immediately after the intervention (wt OR 2.03 cl adj 95% CI 1.25-3.30). CONCLUSIONS: Game jam learning improved cultural safety intentions of Colombian medical students to a similar degree as did a carefully designed lecture and interactive workshop. The game jam was also associated with positive change in participant transcultural self-efficacy. We encourage further research to explore the impact of cultural safety training on patient-related outcomes. Our experience could inform initiatives to introduce cultural safety training in other multicultural settings. TRIAL REGISTRATION: Registered on ISRCTN registry on July 18th 2019. Registration number: ISRCTN14261595 .
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".