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Record W3101665773 · doi:10.36834/cmej.70496

First year medical student experiences with a clinical skills seminar emphasizing sexual and gender minority population complexity

2020· article· en· W3101665773 on OpenAlexafffundvenue
Laurence Biro, Kaiwen Song, Joyce Nyhof‐Young

Bibliographic record

VenueCanadian Medical Education Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Toronto
FundersDepartment of Family and Community Medicine, University of TorontoUniversity of Toronto
KeywordsPreceptorThematic analysisCurriculumMedical educationFocus groupPsychologyPopulationQualitative researchMedicinePedagogySociology

Abstract

fetched live from OpenAlex

PURPOSE: We evaluated first year medical student experiences during a novel four-hour seminar, in which students answered discussion questions, participated in peer role-plays, and interviewed two standardized patients. METHOD: A constructivist qualitative design employed audio-recorded and transcribed student focus groups. Using generic content analysis, transcripts were iteratively coded, emergent categories identified, sensitizing concepts applied, and a thematic framework created. RESULTS: Thirty-five students (71% female) participated in five focus groups. Two themes were developed: SGM bias (faculty, standardized patients [SPs], students, curriculum), and Adaptive Expertise in Clinical Skills (case complexity, learner support, skill development). SPs identifying as SGM brought authenticity and lived experience to their roles. Preceptor variability impacted student learning. Students were concerned when a lack of faculty SGM knowledge accompanied negative biases. Complex SP cases promoted cognitive integration and preparation for clinical work. CONCLUSIONS: These students placed importance on the lived experiences of SGM community members. Persistent prejudices amongst faculty negatively influenced student learning. Complex SP cases can promote student adaptive expertise, but risk unproductive learning failures. The lessons learned have implications for clinical skills teaching, learning about minority populations, and medical and health professions education in general.

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)0.021
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.058
GPT teacher head0.417
Teacher spread0.358 · 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 designObservational
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

Citations10
Published2020
Admission routes3
Has abstractyes

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