Thriving through caring for the self and connecting with others: Lessons of Trans Resilience and Health
Bibliographic record
Abstract
Background Insights from the experiences of transgender and non-binary (TNB) individuals during the COVID-19 pandemic lockdowns reveal various strategies to cope positively, practice self-care, and maintain well-being amidst challenges, stigma, and hardship.Methods Launched before and continuing through the first year of the pandemic, the Trans Resilience and Health study included monthly online data collection among a diverse sample (N = 158) of TNB people living in Michigan, Nebraska, Oregon, and Tennessee. The broader study examined lived experience, resilience, and embodied stress within varied contexts. The current data are a subset of the project data, which entailed a year of monthly surveys (April 2020–March 2021) including questions about responding to COVID-19. Participants responded to this open-ended question: “During this time, what has been most supportive or beneficial to your well-being or your ability to cope with COVID-19 and its impacts on your life?”Results Written responses (n = 1143) revealed varied contributors to resilience including prior experience overcoming challenges, consciously limiting media exposure, spending time in nature, and connecting with others. A conceptual model demonstrates the coping strategies that occur within a broader sociopolitical context contributing to resilience, well-being, and thriving.Conclusions These findings make visible lived experiences of TNB people and strategies for building resilience while underscoring how challenges exact a heavy toll on this population. By detailing these experiences, we can utilize this knowledge to better support these communities in the face of varied forms of hardship, including sustained political targeting and stigma.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".