In the face of adversity: healthcare navigation and strategies of resilience among transgender and nonbinary care-seekers
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".