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Record W4380988486 · doi:10.1177/23821205231183866

An Innovative Master in Anatomy: Combining Anatomy With Educational Scholarship

2023· article· en· W4380988486 on OpenAlexaffabout
Alireza Jalali, Christopher J. Ramnanan

Bibliographic record

VenueJournal of Medical Education and Curricular Development · 2023
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsScholarshipMedical educationAnatomyMedicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.008
Scholarly communication0.0070.003
Open science0.0010.013
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.012
GPT teacher head0.308
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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