Educational leadership during a decade of medical curricular innovation and renewal
Bibliographic record
Abstract
BACKGROUND: The past decade has witnessed successful expansion, distribution and curricular renewal at the University of British Columbia (UBC) medical school. The expansion and distribution of the medical program doubled enrollment and established the first North American medical school training students across multiple geographical locations. The more recent competency-based curriculum renewal demonstrates sustained innovation within UBC medicine. AIMS: This paper describes that a significant contributor to these successes has been a team of teaching faculty whose exclusive roles have been providing curricular support. Over the past decade, this group has evolved into a vital component of the day-to-day operations and performance of the distributed medical curriculum; they now provide continuity in leadership and innovation across multiple educational facets of the program. METHOD/RESULTS: This paper reports on the evolution and significance of these faculty members. The descriptions establish the success of an investment in teaching faculty and underscore the importance of engaging faculty whose primary commitments are to teaching, educational pedagogy, and student support. CONCLUSIONS: This efficacious model of supporting and advancing a complex distributed medical program over more than a decade of pivotal change will be of interest to faculties and programs that are contemplating or navigating similar pursuits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".