Legal gender recognition and the health of transgender and gender diverse people: A systematic review and meta-analysis
Bibliographic record
Abstract
Legal gender recognition (LGR) refers to laws, policies, and administrative procedures that enable transgender and gender diverse (TGD) people to update their legal identity documents (ID) to reflect their self-determined gender. We conducted a systematic review and meta-analysis on the health effects of LGR and a nested scoping review of TGD people’s LGR-related values and preferences (PROSPERO CRD42023441769). We searched seven databases through April 19, 2024, and organization websites (for grey literature) through August 2023. The effectiveness review included quantitative studies evaluating the effect of LGR (policies or possession of gender-concordant ID) on seven domains of health and well-being. We conducted random-effects meta-analyses when possible and otherwise used narrative synthesis. Study risk of bias and confidence in the cumulative evidence were assessed using the ROBINS-E and GRADE, respectively. We screened 2748 studies and included 24 in the effectiveness review. In meta-analyses, LGR was associated with less suicidal ideation (OR=0.75; 95% CI: 0.56-1.00, I 2 = 46%) and psychological distress (e.g., OR for LGR on all versus no ID = 0.53; 95% CI: 0.40, 0.70, I 2 = 17%). LGR may reduce anticipated discrimination, increase healthcare utilization, reduce gonadectomies, and improve socio-economic status, but the evidence was very uncertain. We included 31 studies on TGD persons’ values and preferences. They perceived well-being benefits of LGR, had diverse personal preferences related to safety, and reported financial and policy barriers to LGR. In conclusion, LGR may improve TGD mental health and is perceived to reduce exposure to stigma and discrimination. Higher-quality effectiveness research is needed on other health and well-being outcomes, as well as research to evaluate specific LGR policy provisions. • Systematic review on legal gender recognition (LGR) and health of transgender people • Meta-analyses found less suicide ideation and psychological distress with LGR • Evidence was low or very low certainty for all outcomes • Values and preferences for LGR related to safety and mitigating stigma • More research needed on specific LGR policy provisions
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".