Cancer Screening Barriers in Transgender Persons: An Integrative Review
Bibliographic record
Abstract
Introduction: In Ontario, cancer screenings can detect early signs of breast, cervical, and colorectal cancer and therefore reduce mortality. Transgender persons (TGP) are individuals whose gender assigned at birth differs from their gender identity. TGP face barriers and discrimination with accessing health care. The purpose of this paper is to identify the rate of cancer screening among TGP, compared to cisgender individuals, and identify barriers leading to screening non-adherence.\nMethods: An integrative review of quantitative studies was conducted to explore and summarize current research, the quality of the statistical methods used, and the barriers contributing to decreased cancer screening adherence rates among TGP. To our knowledge, this is the first academic review of quantitative studies examining statistical methods to identify what is known about cancer screening rates of TGP, compared to cisgender individuals.\nResults: Our preliminary results show that although cancer risk is similar among TGP and cisgender individuals, TGP consistently have lower adherence to cancer screening guidelines than cisgender individuals. Discrimination and inadequate provider education on TGP health needs are identified barriers to screening adherence.\nConclusion: Cancer screening can identify the early onset of breast, cervical, and colorectal cancer and reduce mortality. There is decreased cancer screening in TGP compared to cisgender individuals. Providers need to be better educated on the health needs of TGP to close the gap that currently exists in their care. Improving cancer screening adherence among TGP will lessen inequalities in this vulnerable population.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".