Experiences of gender-affirming healthcare among transgender and gender diverse (TGD) people during the COVID-19 pandemic: An explanatory sequential mixed methods study
Bibliographic record
Abstract
Background: Access to timely, gender affirming healthcare (GAH) for transgender and gender diverse (TGD) people is lifesaving. Yet, little is known about the GAH experiences of TGD people during the height of the COVID-19 pandemic. Methods: Utilizing an explanatory sequential mixed methods design, and applying critical transgender and intersectional theories, we examined baseline survey data (n = 44) and follow-up semi-structured interview data (n = 18 qualitative sub-sample from quantitative participants) with a cohort of TGD persons from a COVID-19 eHealth intervention (#SafeHandsSafeHearts) in Toronto, Canada. Results: One-sixth (n = 6/39, 15.4%) of the survey sample reported reduced access to GAH because of COVID-19. Qualitative participants described widespread barriers to access and negative mental health impacts given de-prioritization of TGD health during the pandemic. The cessation of gender affirming surgeries, deemed nonessential, was of greatest concern, along with fear that substantial progress made in improving access to GAH was being unraveled. Nevertheless, community resilience was highlighted through discussion of community support, particularly the development of TGD communities of care to compensate for the lack of institutional support. Despite these struggles, participants embodied trans joy, an act of resistance, refusing to be erased by a system that devalues their healthcare needs. Conclusions: Results inform recommendations for TGD healthcare and improvements that reprioritize TGD community health.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".