Gender, Work, and Health for Trans Health Providers: A Focus on Transmen
Bibliographic record
Abstract
Well-documented health research points to trans people's vulnerability to health inequities that are linked to deeply embedded structural and social determinants of health. Gender and work, as social determinants of health for trans people, both shape and are shaped by multiple factors such as support networks, social environments, income and social status, shelter, and personal health practices. There is a gap in the nursing literature in regards to research on work and health for diverse trans people and a virtual silence on the particular issues of trans-identified health providers. This qualitative study used comparative life history methodology and purposeful sampling to examine links among work, career, and health for transmen who are health providers. Semistructured interviews were completed with four Canadian transmen involved in health care professional and/or practice contexts with diverse professions, age, work, and transitioning experiences. Critical gender analysis showed that unique and gender-related critical events and influences shape continuities and discontinuities in their careerlives. This strength-based approach foregrounds how resilience and growth emerged through participants' articulation with everyday gender dynamics. These findings have implications for nursing research, education, and practice that include an understanding of how trans providers "do transgender work" and supporting them in that process.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".