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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".