Designing an International Faculty Development Program in Medical Education: Capacity and Partnership
Bibliographic record
Abstract
Providing international medical educators with opportunities for faculty development has become a favorable moment for capacity building and the creation of partnerships with universities around the world. It has also become a social responsibility when such a development implies growth and improvement for the institutions involved. In 2018 and 2019, the University of Alberta Faculty of Medicine & Dentistry designed and delivered an international faculty development program (IFDP) in Edmonton, Canada, in collaboration with the faculty management from Jilin University and Wenzhou Medical University, and Shandong University. The inspiration for program driven by capacity development for three universities in China, all of whom were developing strategies to respond to new government policies for medical education. The focus of the course was based on the needs that the three institutions expressed: teaching innovation, research, and quality curriculum development. By design, the two-week, in-person program included lectures, personal tutorials, class and laboratories observations, as well as guided teaching visits to hospitals and university museums. Recommendations are offered to assist other international faculty development programs focused on capacity building for medical education.
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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 | 0.027 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".