Dental School Administrators’ Attitudes Towards Providing Support Services for LGBT‐Identified Students
Bibliographic record
Abstract
A lack of curriculum time devoted to teaching dental students about the needs of lesbian, gay, bisexual, and transgendered (LGBT) health care patient needs and biases against LGBT students and faculty have been reported. Understanding dental school administrators' attitudes about LGBT students' needs might provide further insight into these long-standing issues. The aims of this study were to develop a survey to assess dental administrators' attitudes regarding the support services they believe LGBT-identified students need, to identify dental schools' current diversity inclusion policies, and to determine what types of support dental schools currently provide to LGBT students. A survey developed with the aid of a focus group, cognitive interviewing, and pilot testing was sent to 136 assistant and associate deans and deans of the 65 U.S. and Canadian dental schools. A total of 54 responses from 43 (66%) schools were received from 13 deans, 29 associate deans, and 11 assistant deans (one participant did not report a position), for a 40% response rate. The findings suggest there is a considerable lack of knowledge or acknowledgment of LGBT dental students' needs. Future studies are needed to show the importance of creating awareness about meeting the needs of all dental student groups, perhaps through awareness campaigns initiated by LGBT students.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".