“The Right Story”: Discursive Strategies in Gender-Affirming Healthcare Access
Bibliographic record
Abstract
Despite strides towards a less pathological and diagnostic-centered approach to transgender healthcare, many policies continue to present barriers in accessing gender-affirming medical support that may provide individuals with greater ease in their embodiment. Research on transgender speakers’ linguistic practices in healthcare interactions has shown that practitioners’ prerequisites for granting access to this kind of care remain largely based on criteria presented in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. This practice of evaluation has led to an impression within trans communities that accessing gender-affirming care is a test, where patients must “do” their gender or present their identity in a particular way. Meeting this expectation may feel especially imperative and dehumanizing for non-binary transgender people, as individuals who do not identify as either, or exclusively, masculine or feminine. Presenting a gender positionality outside of the binary has historically meant losing access to care, and as a result, non-binary patients often find themselves using different language to describe their experience than they would in other contexts, simply to gain access to much-needed care. As an intervention in the inequity of doctor-patient communication in this setting, this dissertation considers the metalinguistic (“talk about talk”) observations of non-binary people regarding their gender-affirming healthcare interactions in Ontario. Rather than considering non-binary patients’ practices as straightforward acts of capitulation to medical expectations, their performances in these high-risk interactions are a means to contest their marginal status within the healthcare system. Participant-collaborators express numerous strategies to simplify or obscure their non-binary identities in their pursuit of care, such as the flexible use of identity labels, vocal pitch modulation, the intensification and historicization of experiences of suffering related to gender identity, and avoiding asking questions about their care so as not to compromise doctors’ impressions of their certainty about medical transition. This dissertation thus shows how linguistic performances are a crucial part of the gender-affirming care process; in other words, whether patients get the access that they need often depends not just on what they say, but how they say it, and what they strategically omit.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.026 | 0.072 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".