The Future is Interdisciplinary: Development of a Medical Sciences Master’s Program that Fosters Academic, Professional, and Personal Development
Bibliographic record
Abstract
This paper outlines the design, development, and implementation of a new Master of Science in Interdisciplinary Medical Sciences (MSc IMS) program at Western University in Canada. The course-based program focuses on interdisciplinary education and experiential learning with a goal to foster students' academic, professional, and personal skill development. The authors discuss the rationale for developing the MSc IMS program, describe the curriculum design process, outline the innovative features in the program, and share the findings from the inaugural cohort on their motivation to enroll in the program. The alignment between the data from the inaugural cohort and the rationale for the program is also highlighted. The paper adopts a narrative approach to detail the process used throughout the design of the program and presents findings from a pre- questionnaire—focusing on the overall needs of students and rationale for their enrollment—administered to the inaugural cohort of the program. This scholarly work informs educators, curriculum designers, and administrators about the importance of interdisciplinary programs in higher education. It is also of significant relevance to departments planning to develop new programs or update existing ones to reflect 21st century teaching and learning approaches.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".