First year medical student experiences with a clinical skills seminar emphasizing sexual and gender minority population complexity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".