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Record W2803836451 · doi:10.1002/ase.1797

The design and evaluation of a master of science program in anatomical sciences at Queen's University Canada

2018· article· en· W2803836451 on OpenAlexaffabout
Klodiana Kolomitro, Leslie W. MacKenzie, David Wiercigroch, Lorraine Godden

Bibliographic record

VenueAnatomical Sciences Education · 2018
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsQueen's University
FundersCentre for Teaching and Learning, Universiti Teknologi MalaysiaTulane UniversityAmerican Association of Anatomists
KeywordsCurriculumMedical educationDisciplineProgram Design LanguageTransferable skills analysisProgram evaluationPsychologyHigher educationPedagogyMedicineComputer scienceSociologyPolitical science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.285
Teacher spread0.266 · 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 teacher head, not a consensus.

Study designOther design
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

Citations6
Published2018
Admission routes2
Has abstractyes

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