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Record W3159464868 · doi:10.36834/cmej.71412

Evaluating and implementing an opportunity for diversity and inclusion in case-based learning

2021· article· en· W3159464868 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Medical Education Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsMcGill UniversityUniversity of AlbertaMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsDiversity (politics)Inclusion (mineral)CurriculumResource (disambiguation)Social identity theoryStatement (logic)Social learningSociologyPsychologySocial psychologyComputer sciencePedagogyPolitical scienceSocial group

Abstract

fetched live from OpenAlex

Problem-based learning (PBL) and case-based learning (CBL) often mention social identities only if this information is directly relevant to diagnosis, which can inadvertently perpetuate stereotypes in trainee learning. Using a student-developed resource entitled "Portraying Social Identities in Medical Curriculum: A Primer," we analyzed cases for social identities, identified gaps, and proposed changes, including use of a validated name bank to reflect diversity as represented by local census data. Through this innovation, suggestions were provided to represent the social determinants of health in CBL cases. Other medical schools can use our innovation to improve the social diversity of their medical curriculums.

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.

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)0.008
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, 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 score0.864
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.092
GPT teacher head0.426
Teacher spread0.334 · 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