Outcome measurement for gender-affirming care in Canada: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Gender-affirming care (GAC) includes interventions aimed at supporting an individual's gender identity. Canada is experiencing an increase in referrals for GAC, higher than any other health service; therefore, there is a need for a systematic approach to health outcome measurement to effectively evaluate care. This review aims to analyse health outcome measurement in Canadian GAC, focusing on what is measured, how it is measured and associated barriers and enablers. METHODS: A comprehensive search was conducted in MEDLINE, Embase, PsycINFO, Scopus and CINAHL, up to 26 December 2023. Inclusion criteria were original articles involving transgender or gender-diverse (TGD) patients receiving gender-affirming care in Canada. RESULTS: A total of 4649 articles were identified with 64 included, representing 6561 TGD patients. Most studies were conducted in Ontario (52%), British Columbia (19%) and Quebec (11%). The most common forms of GAC provided were hormonal (36%) and surgical (27%). Barriers to outcome measurement include that most studies (61%) did not use patient-reported outcome measures (PROMs). When PROMs were used, most did not capture gender-related constructs (eg, gender dysphoria). Barriers to accessing care included stigma, discrimination, lack of clinician knowledge, geographic, socioeconomic and institutional barriers. CONCLUSION: This review reveals gaps in outcome measurement for GAC, particularly underutilisation of PROMs and inconsistent outcome measurement and reporting. There is a need to systematically implement PROMs, including those measuring gender-related constructs, to promote patient-centred care. This review provides evidence-based recommendations for improving health outcomes for TGD individuals in Canada.
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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 | 0.025 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".