Exploring Patient Satisfaction among Transgender and Non-Binary Identified Healthcare Users: The Role of Microaggressions and Inclusive Healthcare Settings
Bibliographic record
Abstract
Patient satisfaction is an important indicator of quality of healthcare delivery. Transgender and non-binary (TGNB) people regularly report experiencing discrimination when in healthcare settings and few TGNB-inclusive services are available. Researchers have not examined how discrimination and access to TGNB-inclusive services are associated with patient satisfaction among TGNB healthcare users. Among a convenience sample of TGNB people (n = 146) from Canada and the United States, I examined the relationship between patient satisfaction, experiencing microaggressions from primary healthcare providers, and receiving care in a TGNB-inclusive healthcare setting.\nThe results from a multivariable linear regression suggest that experiencing microaggressions is negatively associated with patient satisfaction while obtaining services from an inclusive healthcare setting is positively associated with satisfaction. These findings emphasize the importance of preparing healthcare providers to engage in inclusive practice with TGNB healthcare users, especially in terms of avoiding microaggressions. They also highlight the importance of creating TGNB-inclusive healthcare settings in fostering patient satisfaction. Researchers, medical professionals, and others working towards health equity, should consider the implications of these findings when developing solutions to improve healthcare access and patient satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".