Lesbian, Gay, Bisexual, and Transgender (LGBT) Issues in Dental School Environments: Dental Student Leaders’ Perceptions
Bibliographic record
Abstract
The objectives of the study reported in this article were to assess dental student leaders' perceptions of educational efforts concerning lesbian, gay, bisexual, and transgender (LGBT) topics and the cultural climate concerning LGBT issues in dental schools in the United States and Canada. In addition, the perceptions of student leaders who self-identified as belonging to the LGBT community and of students with a heterosexual orientation were compared. Data were collected from 113 dental student leaders from twenty-seven dental schools in the United States and three in Canada. Fifty student leaders were females, and sixty-two were males. Only 13.3 percent of the respondents agreed that their dental education prepared them well to treat patients from LGBT backgrounds. The more the student leaders believed that their university has an honest interest in diversity, the better they felt prepared by their dental school program to treat patients from LGBT backgrounds (r=.327; p<.001). The better they felt prepared, the more they perceived the clinic environment as sensitive and affirming for patients with different sexual orientations (r=.464; p<.001). The more they reported that dental schools' administrations create a positive environment for students with LGBT orientations, the more they agreed that persons can feel comfortable regardless of their sexual orientation (r=.585; p<.001). In conclusion, the findings indicate that dental school administrators play an important role in ensuring that future care providers are well prepared to treat patients from LGBT backgrounds and that staff, faculty, students, and patients from these backgrounds are not discriminated against.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".