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The Future is Interdisciplinary: Development of a Medical Sciences Master’s Program that Fosters Academic, Professional, and Personal Development

2024· article· en· W4403603077 on OpenAlexaffvenueabout
Nicole Campbell, Mohammed Estaiteyeh, Isha DeCoito, Amy Robinson

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsBrock UniversityWestern University
Fundersnot available
KeywordsProfessional developmentPersonal developmentSociologyMedical educationPsychologyEngineering ethicsPedagogyMedicineEngineering

Abstract

fetched live from OpenAlex

This paper outlines the design, development, and implementation of a new Master of Science in Interdisciplinary Medical Sciences (MSc IMS) program at Western University in Canada. The course-based program focuses on interdisciplinary education and experiential learning with a goal to foster students' academic, professional, and personal skill development. The authors discuss the rationale for developing the MSc IMS program, describe the curriculum design process, outline the innovative features in the program, and share the findings from the inaugural cohort on their motivation to enroll in the program. The alignment between the data from the inaugural cohort and the rationale for the program is also highlighted. The paper adopts a narrative approach to detail the process used throughout the design of the program and presents findings from a pre- questionnaire—focusing on the overall needs of students and rationale for their enrollment—administered to the inaugural cohort of the program. This scholarly work informs educators, curriculum designers, and administrators about the importance of interdisciplinary programs in higher education. It is also of significant relevance to departments planning to develop new programs or update existing ones to reflect 21st century teaching and learning approaches.

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)0.041
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0120.001
Scholarly communication0.0020.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.206
GPT teacher head0.481
Teacher spread0.275 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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