Transgender and Nonbinary Individuals’ Perceptions Regarding Gender-Affirming Hormone Therapy and Cardiovascular Health: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Transgender and nonbinary individuals face substantial cardiovascular health uncertainties. The use of gender-affirming hormone therapy can be used to achieve one's gender-affirming goals. As self-rated health is an important predictor of health outcomes, an understanding of how this association is perceived by transgender and nonbinary individuals using gender-affirming hormone therapy is required. The objective of this research was to explore transgender and nonbinary individuals' perceptions of cardiovascular health in the context of using gender-affirming hormone therapy. METHODS: In this qualitative study, English-speaking transgender and nonbinary adults using gender-affirming hormone therapy for 3 months or more were recruited from across Canada using purposive and snowball sampling methods. Semistructured interviews were conducted through videoconference to explore transgender and nonbinary individuals' perceptions of the association between gender-affirming hormone therapy and cardiovascular health between May and August 2023. Data were transcribed verbatim, and transcripts were analyzed independently by 3 reviewers using thematic analysis. RESULTS: Twenty-one participants were interviewed (8 transgender women, 9 transgender men, and 3 nonbinary individuals; median [range] age, 27 [20-69] years; 80% White participants). Three main themes were identified: cardiovascular health was not a primary concern in the decision-making process with regard to gender-affirming hormone therapy, the improved well-being associated with gender-affirming hormone therapy was felt to contribute to improved cardiovascular health, and health care provider knowledge and attitude facilitate the transition process. CONCLUSIONS: Gender-affirming hormone therapy in transgender and nonbinary individuals is perceived to improve cardiovascular health. Given the positive associations between care aligned with patient priorities, self-rated health, and health outcomes, these findings should be considered as part of shared decision-making and person-centered care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".