In the face of adversity: healthcare navigation and strategies of resilience among transgender and nonbinary care-seekers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Transgender (trans) and nonbinary people face unique challenges and stigma-related barriers when accessing healthcare services. Yet, how trans and nonbinary care-seekers work to challenge and overcome healthcare adversity remains underexplored. I address this by bridging a strengths-based interview approach with the minority stress and resiliency framework to detail how trans men, trans women, and nonbinary individuals (n = 41) are developing strategies of resilience against entrenched healthcare barriers within Canada. Three main strategies of resilience emerged at the individual- and community levels: at the individual level, the educated self via knowledge acquisition empowered care-seekers to evaluate treatment options and edify providers on gender diversity; at the community level, within community supports worked to alleviate stressors that contributed to healthcare avoidance through the promotion of positive peer relationships, adversity-avoidance, and self-efficacy; additionally, positive healthcare experiences helped rectify feelings of uncertainty, instilling a sense of validation and agency within the healthcare process. Findings showcase how gender-diverse communities are actively working to provide solutions to improve their health outcomes. Broadly, I reveal how resilience can be co-created through a relational process of complex interactions with one’s social network and external resources, offering new insights into resiliency mechanisms among gender-diverse populations.
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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 | 0.000 | 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 | 0.000 | 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 it