The integral role of nurses in primary care for transgender people: A qualitative descriptive study
Bibliographic record
Abstract
AIM: To understand nursing activities, training and key supports needed to provide primary care to transgender individuals. BACKGROUND: Discrimination, limited practitioner knowledge and a deficiency of services contribute to health care barriers for transgender individuals. Literature demonstrating how primary care services are delivered, and more specially the role of nurses in this care, is lacking. METHODS: Qualitative description methodology and interviews were used to describe this phenomenon. Participants included nurse practitioners, registered nurses and registered practical nurses. RESULTS: Nurses are important in providing primary care to transgender individuals. While NPs worked to full scope of practice, RNs' and RPNs' roles could be optimized. A key challenge was lack of education; however, mentorship and collaboration contributed to competency development. Ensuring the workplace provided gender-affirming care was key to a safe and inclusive environment. CONCLUSIONS: Supporting nurses to develop capacity and work to full scope of practice can improve access to care. Ongoing opportunities for mentorship and ensuring an inclusive workplace will aid in the provision of care for this vulnerable population. IMPLICATIONS FOR NURSING MANAGEMENT: Development of organisational policies, staff training and appropriate supports, for role optimization and team collaboration, can eliminate barriers experienced by transgender individuals.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".