Experiences of Transgender People in the Healthcare System: A Complex Analysis
Bibliographic record
Abstract
A nursing perspective following McIntyre and McDonald’s framework was used to unpack the complex issue of challenges faced by transgender people in the Canadian healthcare system, considering historical, ethical, legal, social, cultural, political, and economic perspectives. Transgender people have unique healthcare needs which are often misunderstood or unaddressed by healthcare professionals, leading to poorer outcomes and inequities. Issues concerning transgender people are becoming a focus and a higher priority for society. This literature review reveals the complexity of this issue as the roots in historical, ethical, legal, social, cultural, political, and economic contexts are explored. A variety of barriers and facilitators exist to addressing and resolving this issue, including transgender people avoiding healthcare, intolerance, lack of knowledge and understanding, lack of healthcare provider training, media representation, and economic costs. The analysis of this issue can be used to inform resolution strategies to utilize facilitators and overcome barriers, including increasing awareness and knowledge, improving education and healthcare provider competency, and utilizing nurse leaders as advocates, role models, and agents of change. Improving care of transgender people is a nursing leadership priority. By implementing the suggested resolution strategies, the healthcare system can begin to move towards a more inclusive, understanding, and holistic model of care to improve healthcare access and outcomes for transgender people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".