Setting Students up for Success
Bibliographic record
Abstract
Acknowledging the importance of skill development in graduate programs, Western University in Canada developed an innovative master’s program in interdisciplinary medical sciences. The program aims to promote students’ academic, professional, and personal skills by engaging them in experiential and interdisciplinary learning that adopts an explicit and reflective approach in focusing on seven core skills: problem-solving, communication, leadership, critical reflection, working in diverse teams, project management, and decision making. This paper draws on the experiences and reflections of the inaugural cohort of students enrolled in the program to address the following research questions: 1) How does the MSc IMS program impact students’ skill development? and 2) How did students practise the seven core interdisciplinary skills outlined in the program? The study utilizes a mixed methods approach by collecting quantitative and qualitative data using pre- and post-online surveys administered to the students. The findings highlight the program's positive impact in terms of students’ reflection on their level of competence in the seven core skills, especially in complex problem-solving, oral and written communication skills, and critical reflection. Results also show that students specifically appreciated the contribution of experiential learning components of the program in advancing their skills. The paper emphasizes the importance of addressing students’ skill development in higher education in an explicit and intentional approach and engaging students in reflective practise on their skill development. Implications for the design and review of graduate programs are also discussed.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.003 | 0.037 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.077 | 0.031 |
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".