The National LGBT Cancer Action Plan: A White Paper of the 2014 National Summit on Cancer in the LGBT Communities
Bibliographic record
Abstract
Abstract Despite growing social acceptance of lesbians, gay men, bisexuals, and transgender (LGBT) persons and the extension of marriage rights for same-sex couples, LGBT persons experience stigma and discrimination, including within the healthcare system. Each population within the LGBT umbrella term is likely at elevated risk for cancer due to prevalent, significant cancer risk factors, such as tobacco use and human immunodeficiency virus infection; however, cancer incidence and mortality data among LGBT persons are lacking. This absence of cancer incidence data impedes research and policy development, LGBT communities' awareness and activation, and interventions to address cancer disparities. In this context, in 2014, a 2-day National Summit on Cancer in the LGBT Communities was convened by a planning committee for the purpose of accelerating progress in identifying and addressing the LGBT communities' concerns and needs in the spheres of cancer research, clinical cancer care, healthcare policy, and advocacy for cancer survivorship and LGBT health equity. Summit participants were 56 invited persons from the United States, United Kingdom, and Canada, representatives of diverse identities, experiences, and knowledge about LGBT communities and cancer. Participants shared lessons learned and identified gaps and remedies regarding LGBT cancer concerns across the cancer care continuum from prevention to survivorship. This white paper presents background on each of the Summit themes and 16 recommendations covering the following: sexual orientation and gender identity data collection in national and state health surveys and research on LGBT communities and cancer, the clinical care of LGBT persons, and the education and training of healthcare providers.
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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 | 0.088 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".