A new paradigm for teaching histology in Canada's first distributed medical school
Bibliographic record
Abstract
To address the critical problem of inadequate physician supply in rural British Columbia, the University of BC launched an expanded and distributed medical program in 2005. Medical students engage in a common curriculum at 3 geographic sites across BC, in Vancouver, Prince George and Victoria. The distribution of the core Histology course required a thorough revision of our instructional methodology. We here report our progress and address the question “How can Histology be distributed to remote sites while maintaining the highest of educational standards?” Experience at UBC points to 3 specific challenges: (i) ensuring equitable student access to quality histological images, (ii) implementing a technological infrastructure that allows for real‐time teaching and interactivity across sites, and (iii) ensuring student access at all sites to Faculty expertise. High quality images (available through any internet connection) are provided within a new virtual slide box library of 300 light microscopic and 190 electron microscopic images. Our technological needs are met through a videoconference system that allows for live, interactive sharing of visual/audio materials across the 3 sites. This system also ensures student access to Faculty expertise during didactic teaching sessions. Student examination results and surveys demonstrate that the distribution of our Histology curriculum has been successful.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 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".