The Cancer’s Margins Project: Access to Knowledge and Its Mobilization by LGBQ/T Cancer Patients
Bibliographic record
Abstract
Sexual and/or gender minority populations (LGBQ/T) have particular cancer risks, lower involvement in cancer screening, and experience barriers in communication with healthcare providers. All of these factors increase the probability of health decisions linked with poor outcomes that include higher levels of cancer mortality. Persistent discrimination against, and stigmatization of, LGBQ/T people is reflected in sparse medical curriculum addressing LGBQ/T communities. Marginalization makes LGBQ/T persons particularly reliant on knowledge derived from online networks and mainstream media sources. In what is likely the first nationally-funded and nation-wide study of LGBQ/T experiences of cancer, the Cancer’s Margins project (www.lgbtcancer.ca) conducted face-to-face interviews with 81 sexual and/or gender minority patients diagnosed and treated for breast and/or gynecological cancer in five Canadian provinces and the San Francisco Bay area (US). With specific attention to knowledge access, sharing, and mobilization, our objective was to document and analyze complex intersectional relationships between marginalization, gender and sexuality, and cancer health decision-making and care experiences. Findings indicate that cancer care knowledge in online environments is shaped by cisnormative and heteronormative narratives. Cancer knowledge and support environments need, by contrast, to be designed by taking into account intersectionally diverse models of minority identities and communities. Keywords: biographical knowledge; biomedical knowledge; cancer; cancer care; gender; health disparities; health equity; information access; LGBT health; minority cancer patients; transgender; treatment
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".