An Innovative Master in Anatomy: Combining Anatomy With Educational Scholarship
Bibliographic record
Abstract
The purpose of this article is to describe the design of a unique, bilingual (English and French) Master of Applied Sciences (M.Sc.) in Anatomical Sciences Education (ASE) program at a Canadian postsecondary institution. Anatomy is a core foundational discipline that is essential to many undergraduate, graduate, and professional programs in the health sciences. However, the number of new individuals with the necessary knowledge base and the pedagogical training to teach cadaveric anatomy are in short supply and cannot satisfy the number of openings for trained educators in the field. The M.Sc. in ASE was created to meet the increasingly critical need for instructors trained in human anatomy. The program is designed to prepare students for a career teaching human anatomy to students in the health sciences, emphasizing hands-on cadaveric dissection. Moreover, this program aims to develop educational scholarship skills in trainees by leveraging faculty expertise in medical education research, particularly in the field of anatomy education research. This focus on scholarships will make graduates more competitive for future faculty positions. During their first year of the program, learners will develop clinically relevant anatomy knowledge, teaching skills, and anatomy education scholarship. In the second year, students will benefit from an immediate, hands-on application of this acquired knowledge. In the same year, students will serve as anatomy teachers in the faculty's Medical Program and conduct their education scholarship projects, culminating in a formal research paper. Although similar programs have been developed in recent years, this article provides the first description of the creation of a graduate program in anatomy education. It includes needs assessment, program development, challenges faced, and lessons learned during the approval process. The article serves as a valuable resource for other institutions aspiring to develop similar initiatives.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".