Creating Milestones and Exit Competencies for Medical School Education in Histology
Bibliographic record
Abstract
There is a growing international consensus that the adoption of competency‐based curricula will improve undergraduate medical education. Competencies define measurable behaviours that are expected of graduating medical students and which are attained through a series of progressive milestones that begin on the first day of medical school and continue through to graduation. As part of a curriculum renewal project that embraces milestones and exit competencies, faculty at the medical school at the University of British Columbia (UBC) have generated milestones and competencies in all foundational and clinical medical science domains. This was a significant undertaking for the foundational medical sciences because, although the existing literature provides several examples of medical student exit competencies for clinical specialties, foundational medical science competencies are lacking. We here report on our generation of milestones and exit competencies for a core foundational medical science, histology. The milestones and competencies are brief, coherent and measurable histology‐related skills that will be required of each graduating UBC medical student. Significantly, by framing our competencies with exemplars, we clearly demonstrate how each of our histology exit competencies can be mapped to clinical scenarios that both medical students and post‐graduate physicians will encounter. These examples of milestones and competencies for a core foundational medical science will be useful for educators who are exploring or implementing competency‐based undergraduate medical curricula.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".