The two‐spirit, lesbian, gay, bisexual, transgender, queer, and other sexual and gender identity curriculum in Canadian Dental Schools: What are the gaps and proposed next steps?
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate gaps in the delivery of two-spirit, lesbian, gay, bisexual, transgender, queer, and other sexual and gender identity (2SLGBTQ+) curricula and identify curricular challenges within Canadian dental schools. METHODS: A 21-item closed-ended questionnaire was distributed to curriculum experts across 10 dental schools in Canada. The survey questions were organized into three sections: institution characteristics, current 2SLGBTQ+ content and delivery, and opinions on the improvement of the 2SLGBTQ+ curriculum. Microsoft Excel (2020) was used to perform a descriptive analysis of the survey responses. RESULTS: Nine dental schools participated in the survey. All participating schools reported the offering of undergraduate, graduate, and degree completion programs. The most reported methods of teaching 2SLGBTQ+ content were 'lecture-based teaching' (n = 5), 'small-group teaching' (n = 4), and 'case-based discussions' (n = 4). The most common topics taught were 'gender identity' (n = 7) and '2SLGBTQ+ discrimination in healthcare care settings' (n = 7). The topics of 'sex reassignment surgery,' 'alcohol, tobacco, or other substance use by 2SLGBTQ+ people,' '2SLGBTQ+ pediatric and adolescent oral health issues,' 'coming out,' and 'sex reassignment surgery' were not included or were unknown by the majority of dental schools (n = 8). Overall, participants were unsatisfied with the level of 2SLGBTQ+-specific content covered at their institution and reported a 'lack of space within the curriculum and time constraints' as a barrier to implementation (n = 8). CONCLUSION: Community-based research is needed to identify the unmet oral health needs of the 2SLGBTQ+ population, which can be translated into the development of a risk-based oral health curriculum within Canada and beyond.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".