Developing a bioethics curriculum for medical students from divergent geo-political regions
Bibliographic record
Abstract
Abstract Background The World Health Organization calls for stronger cross-cultural emphasis in medical training. Bioethics education can build such competencies as it involves the conscious exploration and application of values and principles. The International Pediatric Emergency Medicine Elective (IPEME), a novel global health elective, brings together 12 medical students from Canada and the Middle East for a 4-week, living and studying experience. It is based at a Canadian children’s hospital and, since its creation in 2004, ethics has informally been part of its curriculum. Our study sought to determine the content and format of an ideal bioethics curriculum for a culturally diverse group of medical students. Methods We conducted semi-structured interviews with students and focus groups with faculty to examine the cultural context and ethical issues of the elective. Three areas were explored: 1) Needs Analysis - students' current understanding of bioethics, prior bioethics education and desire for a formal ethics curriculum, 2) Teaching formats - students’ and faculty’s preferred teaching formats, and 3) Curriculum Content - students’ and faculty’s preferred subjects for a curriculum. Results While only some students had received formal ethics training prior to this program, all understood that it was a necessary and desirable subject for formal training. Interactive teaching formats were the most preferred and truth-telling was considered the most important subject. Conclusions This study helps inform good practices for ethics education. Although undertaken with a specific cohort of students engaging in a health-for-peace elective, it may be applicable to many medical education settings since diversity of student bodies is increasing world-wide.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".