Underinvested, Under-Referred, and Underserved: Applying a Gender Equity Continuum Framework in Cancer Control Continuum Programs and Policies to Expand to Transgender and Nonbinary Populations
Bibliographic record
Abstract
Gender-inclusive and gender-specific approaches are critically needed in cancer control continuum services to recognize and meet the needs of transgender and nonbinary (trans) populations. Current research, programs, and policies largely cater to cisgender populations and subscribe to a binary, gendered cisnormative ideology, both within health care systems and insurance policies, leaving trans people's cancer prevention and treatment needs neglected. Such disparities can be attributed to the significant gap in funding and research to address trans cancer prevention and treatment. We discuss the research, program, and policy implications of cisnormative practices and provide recommendations for promoting gender-inclusive and specific services across the cancer control continuum with the goal of eliminating cancer disparities and improving cancer outcomes for people of all gender groups, including trans populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".