Biocultural approaches to transgender and gender diverse experience and health: Integrating biomarkers and advancing gender/sex research
Bibliographic record
Abstract
Transgender and gender diverse (TGD) people are increasingly visible in U.S. communities and in national media. With this increased visibility, access to gender affirming healthcare is also on the rise, particularly for urban youth. Political backlash and entrenchment in a gender binary, however, continue to marginalize TGD people, increasing risk for health disparities. The 2016 National Institute of Health recognition of sexual and gender minority people as a health disparities population increases available funding for much-needed research. In this article, we speak to the need for a biocultural human biology of gender/sex diversity by delineating factors that influence physiological functioning, mental health, and physical health of TGD people. We propose that many of these factors can best be investigated with minimally invasively collected biomarker samples (MICBS) and discuss how to integrate MICBS into research inclusive of TGD people. Research use of MICBS among TGD people remains limited, and wider use could enable essential biological and health data to be collected from a population often excluded from research. We provide a broad overview of terminology and current literature, point to key research questions, and address potential challenges researchers might face when aiming to integrate MCIBS in research inclusive of transgender and gender diverse people. We argue that, when used effectively, MICBS can enhance human biologists' ability to empirically measure physiology and health-related outcomes and enable more accurate identification of pathways linking human experience, embodiment, and health.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".