Transgender And Cisgender US Veterans Have Few Health Differences
Bibliographic record
Abstract
Transgender people have been able to serve openly in the military since June 2016. However, the administration of President Donald Trump has signaled its interest in reinstating a ban on transgender military service. In March 2018 President Trump issued a revised memorandum that stated, in part, that people with a "history or diagnosis of gender dysphoria" who "may require substantial medical treatment, including medications and surgery-are disqualified from military service except under certain limited circumstances." Whether and how the health of transgender service members differs from that of cisgender service members (that is, those who identify with their sex assigned at birth) is largely unknown. This study used population-level data for 2014-16 from the Behavioral Risk Factor Surveillance System to compare the health of transgender and cisgender veterans and civilians. An estimated 0.5 percent of veterans in the sample identified themselves as transgender. While transgender civilians had worse health than cisgender civilians across most indicators, very few differences existed among veterans. However, transgender veterans had higher odds of having at least one disability compared to cisgender veterans, despite similar levels of access to health care. These findings largely suggest that transgender veterans do not have worse health than cisgender veterans.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".