Cultural competency preparedness in medical and health professions students ‐ a collaborative study involving anatomy departments at 20 international universities
Bibliographic record
Abstract
Introduction Training in cultural competency skills of medical and health professionals has become an important element of school curricula. Evaluation is often performed via self‐assessment among student cohorts within one country. Only a few studies utilize any standardized and validated tests. Little is known about global comparisons of baseline levels of cultural competency preparedness among students in various health professions. The aim of the study is to assess the baseline level of cultural competency preparedness in junior medical and health professions students at 20 universities from around the world, utilizing a previously validated and standardized testing tool. Results from this study will aid medical educators in the assessment of the extent of cultural competency required to be included internationally in health education curricula. Methods 436 medical and students from various health professions students from 20 universities world‐wide participated via an anatomy‐based student exchange program (80% preclinical medical students). The students were given a validated questionnaire (1) to assess their preparedness in reference to cultural competency prior to the start of the program. The students were also asked to self‐evaluate their cultural competency skills on a 5‐point Likert‐type scale (“none” to “a lot”) encompassing different areas of competency (e.g., knowledge, intrapersonal and interpersonal skills, internal and external outcomes, attitudes). Data were analyzed in Excel for statistical analysis stratified by global region ‐ North America (NA), Europe (EUR), United Kingdom (UK), East Asia (EA), and Australia (AUS) Results Data are presented as means ( M) and their standard deviation. The highest self‐assessment mean was for attitudes toward different cultures (4.4 ± 0.7) and lowest for knowledge about other cultures (3.4 ± 0.8). Regarding the question of general preparedness, the average score was 2.93 (± 1.0) in the validated tool (5‐point Likert‐type scale, “very unprepared” to “well prepared”); 4.6% of students felt “very well prepared”, while 24% felt only “well prepared.” A comparison by region showed the highest scores were from NA (3.14 ± 1.1), and the lowest scores from the UK (mean 2.74 ± 0.9). Regarding preparedness to evaluate patients from different cultures, 7% of students felt “very well prepared”, and 24% felt “well prepared”. Comparison between regions showed that the highest scores were found in EUR (3.1± 0.9). Regarding preparedness to treat patients with limited language proficiency, 14% of students felt that they were “very well prepared” (2.6; ± 1.1). A breakdown by regions showed that the highest scores were found in EUR (2.78 ± 1.0). Regarding preparedness to treat patients from ethnic minorities, 17% felt they were “very well prepared”, and 31% felt “well prepared” (3.4 ± 1.1), with the UK scoring highest in this category (3.56 ± 1.1). Discussion Overall, there appears to be a discrepancy among junior students’ self‐assessments of their cultural competency skills and of their preparedness to treat patients, when compared to standardized test results. Cultural preparedness was similar across the evaluated regions. The data reveal that most regions in the world can benefit from cultural competency training for junior medical and health professions students. Reference 1. Green AR, Chun MBJ, Cervantes MC, Nudel JD, Duong JV, Krupat E, et al. Measuring Medical Students' Preparedness and Skills to Provide Cross‐Cultural Care. Health equity. 2017;1(1):15‐22.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".