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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".