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Record W4318499755 · doi:10.3390/ime2010003

Designing an International Faculty Development Program in Medical Education: Capacity and Partnership

2023· article· en· W4318499755 on OpenAlexaffabout
Martha Burkle, Darryl Rolfson, M Lang

Bibliographic record

VenueInternational Medical Education · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeneral partnershipCurriculumMedical educationCapacity buildingFaculty developmentGovernment (linguistics)ChinaCurriculum developmentPolitical scienceProfessional developmentMedicineSociologyPedagogy

Abstract

fetched live from OpenAlex

Providing international medical educators with opportunities for faculty development has become a favorable moment for capacity building and the creation of partnerships with universities around the world. It has also become a social responsibility when such a development implies growth and improvement for the institutions involved. In 2018 and 2019, the University of Alberta Faculty of Medicine & Dentistry designed and delivered an international faculty development program (IFDP) in Edmonton, Canada, in collaboration with the faculty management from Jilin University and Wenzhou Medical University, and Shandong University. The inspiration for program driven by capacity development for three universities in China, all of whom were developing strategies to respond to new government policies for medical education. The focus of the course was based on the needs that the three institutions expressed: teaching innovation, research, and quality curriculum development. By design, the two-week, in-person program included lectures, personal tutorials, class and laboratories observations, as well as guided teaching visits to hospitals and university museums. Recommendations are offered to assist other international faculty development programs focused on capacity building for medical education.

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)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.455
Teacher spread0.375 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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