Abstract B101: Bridging the gap between cancer care service delivery and the sexual and gender minority communities: a scoping review of the literature
Bibliographic record
Abstract
Abstract Purpose: While there has been an increase in health equity research, there is scarcely research focusing on health equity for cancer survivors who identify as LGBTQIA+ (Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, Asexual, Pansexual and Two-Spirit). This is in part the result of a lack of routine collection of SOGI (Sexual Orientation and Gender Identity) data within the LGBTQIA+ community. Our research aims to analyze and discuss how labelling and terminology have both influenced medical practice and posed barriers and delays in care to the LGBT community over time. Further, examining whether the literature supports increasing LGBT experiences in interprofessional training. By identifying these gaps in the literature and in interprofessional training we hope to decrease barriers to timely and supportive cancer services. Methods: To examine the breadth of research on the LGBTQIA+ community and cancer, a scoping review methodology was utilized to identify knowledge and research gaps. This review examines literature published in the U.S, in academic journals between 1913 and 2021 to gain a greater understanding of how historical contexts, policy, and research have evolved alongside the growing visibility of the LGBTQIA+ community. The authors used a scoping review strategy outlined by Arksey and O’Malley (2005) to review the literature employing a comprehensive list of search terms for cancer and LGBTQIA+. Rayyan software was then utilized to organize the data as our team hand-searched the articles for inclusion (cancer, LGBTQIA) and exclusion (non-US, foreign language, prevention, screening) criteria. A total of 3,290 articles were included in the final analysis. Findings: In 2011, a review of all articles on PubMed demonstrated only 0.3% of all publications pertained to LGBT health-related subject matters, and in a study conducted about U.S and Canadian medical education, research showed that medical students spent < 5 hours, on average, learning about LGBT discrimination and healthcare disparities. 70% of LGBT persons reported discrimination, refusal of care, bias, erroneous assumptions, and derogatory statements during healthcare visits in the United States. Conclusions: There is an urgent need for culturally competent interprofessional care, and we must challenge our approach by not only listening to the needs of the communities we serve but also by being receptive to their feedback. There is an unmet need for education to inform healthcare professionals, health social workers and their community partners of the unique experiences and needs of the LGBTQIA+ community. It is imperative that we use the recommendations and guidelines provided by the LGBTQIA+ cancer patient population to transform their healthcare experiences, improve patient-provider communication, and provide precision-supportive care. Citation Format: Brittany M. Hernandez, Sanford E. Jeames, Lailea Noel. Bridging the gap between cancer care service delivery and the sexual and gender minority communities: a scoping review of the literature [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr B101.
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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 | 0.029 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.041 | 0.038 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".