Integrated Histology and Pathology Education in the Renewed UBC M.D. Undergraduate Program
Bibliographic record
Abstract
To meet changing and future societal health care needs, the education of medical students is shifting in new directions and renewals of undergraduate medical school curricula are occurring across Canada, including at the Faculty of Medicine (FoM) at the University of British Columbia (UBC). The UBC FoM introduced a renewed undergraduate medical curriculum in the Fall of 2015. Histology and pathology are core foundational medical sciences related to normal and abnormal bodily processes and, as part of the renewed curriculum, faculty leaders developed an innovative and practical approach to introduce students to the two disciplines. The emphasis is on an integrated approach that makes sessions meaningful and interesting. Laboratory sessions are the focus of the integration: students first explore virtual slides to learn the normal histology of a tissue or organ and are then guided through virtual slides of prototypical pathologies of the same tissue or organ. The intent is that medical students concurrently learn both how important it is to understand normal tissue architecture/cellular morphologies and the effects that disease states can have on them. Student feedback demonstrated that the integration is a great success: 92% of responding first‐year UBC medical students strongly/completely agreed that the pathology integration successfully demonstrated the clinical relevance of histology, and 89% strongly/completely agreed that our approach to the pathology integration was appropriate and useful for identifying prototypical pathologies. Our experiences demonstrate an efficacious approach for integrating disciplines and will be useful for medical science educators who are exploring or implementing interdisciplinary undergraduate medical education.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".