A Scoping Review of Oral Health Outcomes and Oral Health Service Utilization of 2SLGBTQ+ People
Bibliographic record
Abstract
BACKGROUND: Oral health is an integral aspect of overall well-being and quality of life. Population groups such as two-spirit, lesbian, gay, bisexual, transgender, and queer, including other sexual and gender minorities (2SLGBTQ+), have reported poor oral health outcomes. Therefore, the aim of this review was to investigate the extent and scope of the literature describing 2SLGBTQ+ oral health outcomes, including unmet oral health needs and patterns of oral health care service utilization, as well as the risk factors affecting both. METHODS: A comprehensive search strategy was developed to review the scope of the literature pertinent to unmet oral health needs and factors affecting access to oral health care among 2SLGBTQ+ members, globally. In total, 6 databases were searched with a combination of keywords relevant to 2SLGBTQ+ oral health status and oral health care utilization. RESULTS: Our review identified 10 studies that met the eligibility criteria. Five out of 10 studies were based in India, 4 in the United States, and 1 in Brazil. Two studies reported poorer oral health outcomes among transgender people as compared with cisgender people, while 2 studies reported similar patterns of dental service utilization between their transgender and cisgender participants. Five studies explored the personal and structural risk factors associated with poor oral health outcomes, including financial affordability and income level and perceived discrimination, including instances of misgendering in health care settings. However, further comprehensive studies must be conducted to validate the trends and findings reported by the studies in the review and to generate data from diverse regional contexts. CONCLUSIONS: Our review identified that the extent of the literature in this research area is sparse and scarce. The evidence indicates poorer oral health status among 2SLGBTQ+ communities. Wider studies with diverse, representative samples are required to gain a comprehensive understanding of 2SLGBTQ+ oral health outcomes. KNOWLEDGE TRANSFER STATEMENT: The results of this review will undoubtedly be important for many years to come as 2SLGBTQ+ oral health equity is prioritized by experts in public health dentistry. This review will allow other researchers to understand and fill literature gaps regarding 2SLGBTQ+ oral health outcomes, furthering this area of research.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".