Examining the Experiences of Transgender and Non-Binary Nursing Students and Nurses in Canada
Bibliographic record
Abstract
BackgroundTransgender and non-binary (TGNB) individuals face significant discrimination and underrepresentation in healthcare, particularly within the nursing workforce. These challenges often lead to increased stress, limited career opportunities, and the concealment of identities.PurposeThis study investigates the experiences and challenges faced by TGNB nursing students and nurses in Canada.MethodsAn online anonymous questionnaire, featuring both closed and open-ended questions, was used to gather data from participants recruited through social media and nursing networks across Canada.ResultsThe survey was completed by 101 participants, with most being nursing students (54.5%) or registered nurses (20.8%), and predominantly practicing in Ontario (53.5%). While many participants were open about their TGNB identity, they reported insufficient TGNB representation and inadequate education on TGNB healthcare. Disclosing their gender identity remained difficult primarily due to fear of rejection, with 43.6% encountering barriers related to their gender identity when applying to nursing programs. Additionally, 67.3% experienced derogatory comments personally, and 66.3% witnessed similar remarks towards the TGNB community. Among 46 nurse participants, 84.7% have noticed discrimination towards TGNB patients during care and 80.4% towards a peer, colleague or superior. Only 29.7% reported to have received education on TGNB topics, which was often superficial.ConclusionsThe study reveals significant challenges for TGNB individuals in nursing education and the workforce, including discrimination, inadequate educational content, and barriers in professional settings. These findings highlight the need for more inclusive, supportive, and comprehensive education on TGNB healthcare to create a more equitable environment for TGNB nurses and patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".