An Adapted Behavioral Framework for Integrating LGBT+ in Dental Curriculum: Learner‐Centered Training to Person‐Centered Care
Bibliographic record
Abstract
Lesbian, gay, bisexual, transgender, or other sexual orientations and gender identities (LGBT+) people report poorer oral health outcomes compared to their heterosexual and gender-binary counterparts due to social and structural inequities. As such, there is a need for robust integration of social determinants of health (SDOH) and their intersectionality with oral health among LGBT+ people. An SDOH framework was adapted, based on education, organization, and community domains, to integrate the LGBT+ teaching and content into already established dental curricula. The education domain emphasizes the integration of didactic and experiential education to address the person-centered oral health needs of sexual and gender minorities. This includes didactic content delivery by LGBT+ people and representation from diverse gender and sexual backgrounds in case-based learning and community service-learning. The organization domain encourages the embedment of health equity and the development of inclusive environments supportive of gender and sexual minorities into the mission statements of dental schools and the continuing professional development. Important measures include the integration of preferred pronouns at all levels of the organization, diverse gender representation on patient intake forms, and dedicated safe spaces for all minorities, including sexual and gender minorities. Lastly, the community domain emphasizes the development of partnerships between LGBT+ community organizations and dental schools to develop community-integrated educational models for the teaching of SDOH and the addressal of unmet LGBT+ oral health needs. Integrating this adapted SDOH framework will provide learners, faculty, and staff with a comprehensive understanding of the person-centered needs of LGBT+ community members. This will encourage learners to approach gender and sexual minorities with empathy and cultural humility while providing trauma-informed, person-centered 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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".