Teaching Trans-Centric Curricular Content Using Modified Jigsaw
Bibliographic record
Abstract
Introduction: Transgender (trans) individuals have unique medical needs and difficulty accessing quality health care, exacerbated by inadequate provider knowledge. Incorporation of trans health care into medical school curricula has increased recently to address this gap. Jigsaw activities emphasize positive interdependence through structured cooperative learning, resulting in increased interest and self-confidence. We implemented a voluntary 2-hour modified jigsaw exercise on trans health care with changes designed to optimize the structure for medical students. Methods: The session was implemented both in person and virtually over 2 years with preclerkship medical students at the end of their endocrine/reproduction physiology course. The session featured a knowledge test with answer discussion followed by a clinical correlation-either a case discussion or video discussion. A pre- and posttest design compared students' knowledge, attitudes, and beliefs. Results: Eighty-nine students participated. Their initial attitudes and beliefs regarding trans health care were highly positive and remained elevated. Participants showed increases in knowledge and self-confidence discussing gender identity and clinical care postsession. All expressed interest in further training and felt the session enhanced their understanding of trans health and reproductive physiology. On 1-year follow-up, students showed decreased knowledge and self-confidence in discussing trans health; however, scores remained higher than presession. Student surveys suggested formal integration of more trans health education into the curriculum. Discussion: Medical students increased their knowledge and self-confidence regarding trans medicine and felt the modified jigsaw exercise was an effective teaching method. The results suggest that ongoing education is an important tool in optimizing trans health care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".