Development, Implementation, and Effectiveness of a Self-sustaining, Web-Based LGBTQ+ National Platform: A Framework for Centralizing Local Health Care Resources and Culturally Competent Providers
Bibliographic record
Abstract
BACKGROUND: The lesbian, gay, bisexual, transgender, queer, and other sexual and gender minority (LGBTQ+) population has long faced substantial marginalization, discrimination, and health care disparities compared to the cisgender, heterosexual population. As the etiology of such disparities is multifaceted, finding concrete solutions for LGBTQ+ health care equity is challenging. However, the internet may offer the space to initiate an effective model. OBJECTIVE: In an effort to make LGBTQ+ public resources and culturally competent providers transparent, modernize medical education, and promote cultural competency, OutCare Health-a nonprofit 501(c)(3) multidisciplinary, multicenter web-based platform-was created. METHODS: The organization employs a cyclic, multidimensional framework to conduct needs assessments, identify resources and providers, promote these efforts on the website, and educate the next generation of providers. LGBTQ+ public health services are identified via the internet, email, and word of mouth and added to the Public Resource Database; culturally competent providers are recruited to the OutList directory via listservs, medical institutions, local organizations, and word of mouth; and mentors are invited to the Mentorship Program by emailing OutList providers. These efforts are replicated across nearly 30 states in the United States. RESULTS: The organization has identified over 500 public health organizations across all states, recognized more than 2000 OutList providers across all states and 50 specialties, distributed hundreds of thousands of educational materials, received over 10,000 monthly website visits (with 83% unique viewership), and formed nearly 30 state-specific teams. The total number of OutList providers and monthly website views has doubled every 12-18 months. The majority of OutList providers are trained in primary, first point-of-care specialties such as family medicine, infectious disease, internal medicine, mental health, obstetrics and gynecology, and pediatrics. CONCLUSIONS: A web-based LGBTQ+ platform is a feasible, effective model to identify public health resources, culturally competent providers, and mentors as well as provide cultural competency educational materials and education across the country. Such a platform also has the opportunity to reach self-perpetuating sustainability. The cyclic, multidisciplinary, multidimensional, multicenter framework presented here appears to be pivotal in achieving such growth and stability. Other organizations and medical institutions should heavily consider using this framework to reach their own communities with high-quality, culturally competent care for the LGBTQ+ population.
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 | 0.068 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| 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".