Need for more research on and health interventions for transgender people
Bibliographic record
Abstract
Background Recently, lesbian, gay, bisexual, and transgender (LGBT) scientific production is growing, but transgender (TG) people is less considered in the LGBT-related research, highlighting the lack of representative data on this neglected population. METHODS: To assess the current status of scientific production on TG population, a bibliometric study was performed using the articles on TG people deposited in five databases, including PubMed/Medline, Scopus, Science Citation Index (SCI), Scientific Electronic Library Online (SciELO) and Latin American and Caribbean Health Sciences Literature (LILACS). RESULTS: The PubMed/Medline search retrieved 2370 documents, which represented 0.008% of all articles recorded in Medline. The Scopus search identified 4974 articles. At SCI, 2863 articles were identified. A search of the SciELO database identified 39 articles, whereas the LILACS search identified 44 articles. Most papers were from the US (57.59%), followed by Canada (5.15%), the UK (4.42%), Australia (3.19%), The Netherlands (2.46%) and Peru (1.83%). These six countries accounted for 74.6% of all scientific output. CONCLUSIONS: The findings indicate that the TG-related research is low, especially in low-income developing countries, where stigma and discrimination are common. More awareness, knowledge, and sensitivity in healthcare communities are needed to eliminate barriers in health attention and research in this population.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".