Transphobia rather than education predicts provider knowledge of transgender health care
Bibliographic record
Abstract
PURPOSE: Transgender and gender diverse (TGD) patients face significant hurdles in accessing affirming, knowledgeable care. Lack of provider knowledge presents a substantial barrier to both primary and transition-related care and may deter patients from seeking health care. Little is known about factors that affect provider knowledge or whether exposure to TGD health content during training is associated with improved knowledge among providers. Using the TGD Healthcare Knowledge Scale, this study aimed to determine whether prior education on TGD health predicts clinicians' current knowledge regarding health care for TGD patients. METHODS: An online survey examining exposure to TGD content and knowledge of TGD health care was distributed to all primary care providers in an integrated health care system in the Midwestern United States. Multivariable linear regression was used to predict provider knowledge, controlling for demographics, transphobia and other potential confounders. RESULTS: The response rate was 57.3% (n = 223). The mean knowledge score was 7.41 (SD = 1.31) on a 10-point scale. Almost half (48.4%, n = 108) had no formal education on TGD health care, yet half (49.7%, n = 111) of providers reported previously caring for at least one transgender patient. In regression analysis, provider knowledge of TGD health care was associated with transphobia (β = -0.377, 95% CI = -0.559 to -0.194, p < 0.001), but not with hours of formal education (β = -0.027, 95% CI = -0.077 to 0.023, p = 0.292) or informal education (β = -0.012, 95% CI = -0.033 to 0.009, p = 0.259). CONCLUSIONS: Increasing hours of education related to TGD health care may not be sufficient to improve providers' competence in care for TGD individuals. Transphobia may be a barrier to learning that needs to be addressed. Broader efforts to address transphobia in society in general, and in medical education in particular, may be required to improve the quality of medical care for TGD patients.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".