The Lived Experiences of Nurses Who Identify as 2SLGBTQIA+ in Providing Nursing Care Within an Urban Prairie Setting: An Interpretive Descriptive Proposed Study
Bibliographic record
Abstract
Background: Protections for those who identify as 2SLGBTQIA+ have improved, yet discrimination against people who identify as non-heterosexual and non-cisgender frequently occurs. Literature displays that heteronormativity and cisnormativity prevail within the health care system in Canada. Nurses identifying as 2SLGBTQIA+ are hypothesized to be a large subcategory within the profession. There is little literature on the experiences of nurses identifying as 2SLGBTQIA+. The literature indicates that health care patients feel more comfortable when their providers share similar identities. Purpose: This study aims to unveil the lived experiences of equity-deserving nurses in providing nursing care in an urban prairie setting. What are nurses' experiences identifying as 2SLGBTQIA+ in providing nursing care in Winnipeg, Manitoba, Canada? What do nurses identifying as 2SLGBTQIA+ believe their identity brings to their nursing practice and patient care? Methods: This qualitative study will use semi-structured interviews to gather data. Interviews will be transcribed and coded to generate descriptive themes of the lived experiences of equity-deserving nurses. Minority Stress Theory will be used to understand the experiences of study participants. Convenience and snowball sampling will be employed to recruit nurse participants. Conclusion: The findings of this study will contribute to the literature on the experiences of a large group of equity-deserving nurses. Understanding the experiences of 2SLGBTQIA+ nurses will provide meaningful implications for nursing curriculum development and policy change. This study will provide supporting data for increasing representation and diversity in nursing and improving patient outcomes.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".