The design and evaluation of a master of science program in anatomical sciences at Queen's University Canada
Bibliographic record
Abstract
The purpose of this study was to describe the design and evolution of a unique and successful Master of Science program in anatomical sciences at one Canadian post-secondary institution and to evaluate its long-term impact on student learning. This program prepares students to teach anatomy and design curricula in the anatomical sciences and is structured around three pillars of competency-content (disciplinary knowledge and transferable skills), pedagogy, and inquiry. Graduates of the program from the last ten years were surveyed, to better understand the knowledge, skills, and habits of mind they have adopted and implemented since completion. Interest was taken in identifying aspects of the program that students found particularly beneficial and areas that needed to be further developed. Based on the findings, this program has been a highly valuable experience for the graduates especially in helping them develop transferable skills, and grow as individuals. The hope is that other institutions that have similar programs in place or are considering developing them would benefit from this description of the program design and the sharing of the lessons learned.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".