The importance of multiculturalism in medical education: a global comparison of perspectives from medical and health professions students at 21 universities
Bibliographic record
Abstract
Objectives This study aims to quantitatively assess the baseline level of self-perceived cultural competency preparedness and skillfulness among medical and health professions students from 21 universities around the world utilizing a previously validated and standardized testing tool. Design Cross-sectional study. Setting The International Collaboration and Exchange Program (ICEP), a global exchange initiative for junior medical and health professions students spanning 21 universities across four continents. Participants A total of 753 students from the 2021 and 2022 ICEP cohorts. Main Outcome Measures Students self-evaluated their cultural competency skills on a 5-point Likert-type scale encompassing different areas of competency. Multiple linear regression was performed to identify contributors to cultural competency levels. Results Upon rating how skillful they are at interacting with culturally diverse patients, North American students reported the highest scores with a mean of 3.22, while Australian students showed the lowest score of 2.82. When analyzing students’ stages of study, those in clinical years of medical schools scored the highest at 3.29. Significant variations were observed in the cultural competency self-rating scores among students based on their respective regions ( p < .005) and program types/stages ( p < .05). Notably, students in their clinical years of school consistently rated themselves higher compared to their preclinical counterparts ( p < .05). Furthermore, students from Europe displayed elevated self-ratings compared to the other regions ( p < .005). Conclusions Though these participants represent a highly motivated subgroup of students, potentially limiting result generalizability, the findings emphasize that regional differences exist. Given the multifaceted nature of cultural competency, the results suggest that factors such as educational stage, age, and region may influence students’ perceived competency levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".