Gynecologic Health Care Providers' Willingness to Provide Routine Care and Papanicolaou Tests for Transmasculine Individuals
Bibliographic record
Abstract
Background: Transmasculine individuals who have a cervix may be at risk of cervical cancer, but they face a number of barriers to accessing care, including difficulty finding knowledgable and culturally sensitive providers who are willing to care for transgender patients. We examined gynecologic health care providers' willingness to provide routine care and Papanicolaou tests (Pap tests) to transmasculine individuals, including the role of personal, clinical, and professional factors. Materials and Methods: We surveyed attending physicians, advanced practitioners, and residents in the Women's Health department of a large, integrated Midwest health system ( n = 60, 74.1% response rate). Results: A majority of participants were female (68.3%) and white (73.3%). Most had met a transgender person before (79.7%), and 40.7% had cared for a transgender patient in the past 5 years. Most reported willingness to provide routine care (74.6%) and Pap tests (85.0%) to transmasculine people. Bivariate analysis suggests that having met a transgender person ( p = 0.028), higher empathy scores ( p = 0.015), political views ( p = 0.0130), and lower transphobia ( p = 0.012) were associated with willingness to provide routine care to transmasculine individuals. Lower transphobia ( p = 0.034) and political views ( p < 0.001) were also associated with willingness to provide Pap tests to transmasculine people. Conclusions: Providers' willingness was not associated with barriers related to training or knowledge—only with personal biases and experiences. Transgender-inclusive health care training that addresses personal attitudes should be a routine part of training for all health professionals.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".