<i>Cancer's Margins:</i> Trans* and Gender Nonconforming People's Access to Knowledge, Experiences of Cancer Health, and Decision-Making
Bibliographic record
Abstract
PURPOSE: Research in Canada and the United States indicates that minority gender and sexuality status are consistently associated with health disparities and poor health outcomes, including cancer health. This article investigates experiences of cancer health and care, and access to knowledge for trans* and gender nonconforming people diagnosed with and treated for breast and/or gynecologic cancer. Our study contributes new understandings about gender minority populations that will advance knowledge concerning the provision of culturally appropriate care. This is the first study we are aware of that focuses on trans* and gender nonconforming peoples' experiences of cancer care and treatment, support networks, and access to and mobilization of knowledge. METHODS: This article analyzes trans* and gender nonconforming patient interviews from the Cancer's Margins project ( www.lgbtcancer.ca ): Canada's first nationally-funded project that investigates the complex intersections of sexual and/or gender marginality, cancer knowledge, treatment experiences, and modes of the organization of support networks. RESULTS: Our analysis documents how different bodies of knowledge relative to cancer treatment and gendered embodiment are understood, accessed, and mobilized by trans* and gender nonconforming patients. Findings reported here suggest that one's knowledge of a felt sense of gender is closely interwoven with knowledge concerning cancer treatment practices; a dynamic which organizes knowledge mobilities in cancer treatment. CONCLUSIONS: The findings support the assertion that cisgender models concerning changes to the body that occur as a result of biomedical treatment for breast and/or gynecologic cancer are wholly inadequate in order to account for trans* and gender nonconforming peoples' experiences of cancer treatments, and access to and mobilization of related knowledge.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".