Characterization of the literature informing health care of transgender and gender-diverse persons: A bibliometric analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Transgender and gender-diverse (TGD) persons experience health inequities compared to their cisgender peers, which is in part related to limited evidence informing their care. Thus, we aimed to describe the literature informing care provision of TGD individuals. DATA SOURCE, ELIGIBILITY CRITERIA, AND SYNTHESIS METHODS: Literature cited by the World Professional Association of Transgender Health Standards of Care Version 8 was reviewed. Original research articles, excluding systematic reviews (n = 74), were assessed (n = 1809). Studies where the population of interest were only caregivers, providers, siblings, partners, or children of TGD individuals were excluded (n = 7). Results were synthesized in a descriptive manner. RESULTS: Of 1809 citations, 696 studies met the inclusion criteria. TGD-only populations were represented in 65% of studies. White (38%) participants and young adults (18 to 29 years old, 64%) were the most well-represented study populations. Almost half of studies (45%) were cross-sectional, and approximately a third were longitudinal in nature (37%). Overall, the median number of TGD participants (median [IQR]: 104 [32, 356]) included in each study was approximately one third of included cisgender participants (271 [47, 15405]). In studies where both TGD and cisgender individuals were included (n = 74), the proportion of TGD to cisgender participants was 1:2 [1:20, 1:1]. Less than a third of studies stratified results by sex (32%) or gender (28%), and even fewer included sex (4%) or gender (3%) as a covariate in the analysis. The proportion of studies with populations including both TGD and cisgender participants increased between 1969 and 2023, while the proportion of studies with study populations of unspecified gender identity decreased over the same time period. CONCLUSIONS: While TGD participant-only studies make up most of the literature informing care of this population, longitudinal studies including a diversity of TGD individuals across life stages are required to improve the quality of evidence.
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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 | 0.041 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.232 | 0.214 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".