In Our Own Words: A Qualitative Exploration of Complex Patient-Provider Interactions in an LGBTQ Population
Bibliographic record
Abstract
While sexual and gender minorities are at increased risk for poor health outcomes, there is limited data regarding patient-provider interactions. In this study, we explored the perspectives of LGBTQ patients and their encounters with physicians in order to improve our understanding of patient-physician experiences. Using purposive selection of self-identified LGBTQ patients, we performed fourteen in-depth semi-structured interviews on topics of sexual orientation and gender identity, as well as their perceived role in the patient-provider relationship. Coding using a modified grounded theory approach was performed to generate themes. We identified three major themes that demonstrate the complexity of LGBTQ patient experiences. The first, Lacking trust, identifies mistrust and loss of the physician-patient relationship resulting from physicians’ poor or judgmental communication, or from physicians making assumptions about gender, using incorrect pronouns, and not recognizing heterogeneity within the transgender community. A second theme, Being vulnerable, describes the challenges and fears related to comfort of patients with disclosing their sexual orientation and/or gender identity. A final theme, Navigating discrimination, outlines racial or ethnic discrimination which creates an additional burden on top of illness and stigmatized identity. Our results reveal the complex needs of individuals with multiple stigmatized identities when developing relationships with providers. By using an intersectional perspective that appreciates the plurality of patients’ identities, providers can help to improve their relationships with LGBTQ patients. Incorporating intersectional training for medical students and residents could greatly benefit both LGBTQ patients and their physicians.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".