Beyond representation: Taking concrete action to move towards inclusion and social justice in specialty nursing education
Bibliographic record
Abstract
Inclusivity and equity are clear priorities in nursing education today. In the British Columbia Institute of Technology Specialty Nursing Department, concepts of diversity, equity, and inclusion underpin all our nursing pedagogical approaches including clinical practice, theory, and simulation. However, operationalizing these ideas into meaningful activities can be challenging. Here we showcase a concrete educational approach to addressing inclusivity and equity in nursing education in the form of an assignment grounded in equity-oriented care principles that can be adapted for both academic and clinical nursing education contexts. All healthcare providers share accountability for decolonization, anti-discrimination, and equity-oriented approaches to health care. Nursing educators have both the opportunity and responsibility to support the upcoming generation of nurses and specialty nurses to address health inequities at the point of care by first understanding the disparities in their healthcare system. These assignments are a practical and applicable way to begin to plant the seeds of cultural change within the nursing profession, ultimately empowering nursing leaders on the frontline.
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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 | 0.046 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.046 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".