Foregone Healthcare: A Secondary Analysis of Survey Data on the Experiences of a Sample of Transgender and Non-Binary in Nova Scotians
Bibliographic record
Abstract
Introduction: Previous international research has identified transgender and non-binary populations have poor health outcomes, due in part to foregone healthcare.Objective: This study focuses on examining the healthcare challenges in accessing gender-affirming care among a sample of transgender and non-binary Nova Scotians, and how these experiences may contribute to foregone healthcare.Methods: This research utilized secondary data analysis of a subset of data from an existing province-wide online survey was conducted of transgender and non-binary Nova Scotians. The methodology of this study employs both descriptive statistics and thematic analysis of close-ended and open-ended survey response categories. The socioecological model was used as the conceptual framework to describe the various levels of influence contributing to foregone healthcare among this population.Results: Three main themes emerged from the data, including fear of discrimination leading to decreased quality of care, poor availability of transgender and non-binary specific health services, and perceived or actual low levels of cultural competency among healthcare providers.Conclusion/Discussion: These factors contributed to foregone healthcare within this population which in turn led to a number of recommendations to improve patient-provider interactions. Specifically, we recommend additional resources and training for health care providers and trainees to improve their cultural competency in providing gender-affirming care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".