Teaching Lesbian, Gay, Bisexual, and Transgender Issues in Dental Education: A Multipurpose Method
Bibliographic record
Abstract
Discussion of lesbian, gay, bisexual, and transgender (LGBT) issues has fallen short in dental curricula. This article aims to describe the didactic approach used to present issues of sexuality in the D.M.D. curriculum at the University of British Columbia. This descriptive article discusses the main pedagogies employed to present and discuss LGBT issues: lecture-based seminars, guest panel discussion with members of the LGBT community, poster discussion, and student reflections on the topic of sexuality. The approach to sexual diversity presented here does not profess to make an otherwise homophobic student LGBT-friendly, but it exposes all students to alternative views of sexuality, challenges their values and beliefs, and celebrates diversity. The methodology presented has had a positive impact upon students as illustrated by their reflections, but further discussion is needed to better understand the implications of LGBT issues in both academic and professional settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".