Bibliographic record
Abstract
This workshop is the product of a research study exploring the strategies that queer people develop to navigate hegemonic, heteropatriarchal health care systems, and ways that nurse education can incorporate a narrative-based, whole person care approach to understanding and supporting the needs of queer patients. This mixed-methods study included interviews with queer people, nurse educators and practicing nurses; textual analysis of queer health narratives; close reading of queer, feminist and cultural theory; and autoethnography.Some of the questions that we will explore are: How do queers use personal narratives to help navigate health care systems not designed to see/meet their needs? How do queers challenge dominant power structures in medicine? What does whole person care look like in a queer context? What would nurses like to see included in nursing education, and what do queers want health providers to know? What are the key pedagogical challenges in attempting such communication?The stories that queer people carry with them to medical encounters are a rich and underutilized resource for health care providers, and a tool for patients trying to manage serious or chronic illness. We will explore methods for including storytelling in nursing education as well as patient care, and participants will engage in a narrative medicine/autoethnographic exercise.We hope participants will leave our workshop with a better understanding of queer peoples' experiences of health care, and ways that queers and nurses can work together for better health 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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".