Cancer Screening and Prevention in the Transgender and Gender Diverse Population: Considerations and Strategies for Advanced Practice Nurses
Bibliographic record
Abstract
OBJECTIVES: This discussion paper presents recent evidence regarding cancer screening and prevention among the transgender and gender diverse (TGD) community and highlights where and how advanced practice nurses (APNs), particularly those in primary care, can better contribute to closing the gap between healthcare disparities between TGD and cisgendered populations. METHODS: Relevant publications on the topic and professional guidelines and evidence have formed the basis for this discussion paper. RESULTS: TGD individuals are a vulnerable population with unique needs. They remain at risk of cancer and might be at greater risk of developing some cancers compared to cisgendered people but are underscreened. Barriers to gender-affirming care need to be addressed to improve access to prevention and screening services and improve the cancer care experiences and outcomes of TGD people. CONCLUSION: APNs can work in collaboration with TGD individuals and the healthcare system to improve access to culturally safe cancer screening and more effective prevention of cancer and poor cancer outcomes. IMPLICATIONS FOR NURSING PRACTICE: APNs have the potential to improve access to cancer screening for TGD people by increasing their understanding of the needs of the population, providing culturally safe care, and advocating for more preventative care and cancer screening. With greater knowledge and understanding of the needs and preferences of TGD people both broadly and in relation to cancer screening and prevention, targeted interventions and care approaches can be implemented. APNs should also aim to conduct evaluations and research into cancer prevention and screening to build the currently limited evidence base and nursing knowledge in this important field.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".