Cultures with Unique Nutrition Concerns: Lesbian, Gay, Bisexual, Transgender
Bibliographic record
Abstract
Learning Outcome: There is a lack of data on nutrition issues of concern to the Lesbian, Gay, Bisexual and Transgender community. Lesbian, Gay, Bisexual and Transgender (LGBT) individuals include all races, ethnicities, religions, and social classes. 7–10% of Canadians and 4% of Americans identify as LGBT. LGBT experience unique health disparities, as noted in Healthy People 2020. With increasing awareness and acceptance of LGBT, more individuals are acknowledging their identity. This study assessed the literature on nutrition issues of relevance to LGBT, and the availability of appropriate nutrition education materials. PUBMED was searched using the terms female and male homosexuality, gay, bisexual, transsexualism, transgender, and nutrition and health from 1998–2012. None of the articles addressed nutrition issues. Lesbians and bisexual females are more likely to be overweight or obese, contributing to an increased risk of heart disease, diabetes, and certain cancers. Gay and bisexual males have a higher rate of eating disorders and body dissatisfaction. Transgendered undergoing hormone therapy are at higher risk for cardiovascular disease, decreased bone density, and impaired glucose metabolism, thus requiring specialized nutrition counseling. LGBT have the highest rates of abuse of tobacco, alcohol, and other drugs, which may affect their nutrition status. Healthcare providers need to be aware of the special nutrition needs of LGBT. Despite increased risks, there is little research done or nutrition education materials available to address these concerns. Funding Disclosure: None
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".