Diverse Representation in Nursing Leadership: Developing a Shared Position Statement on Allyship
Bibliographic record
Abstract
Diversity initiatives are being implemented widely within academia and society more broadly; however, the School of Nursing (SoN) at Dalhousie University in Halifax, Nova Scotia, Canada, is taking an innovative approach. Faculty members recognized the need to support students at the graduate and undergraduate levels from Black, Indigenous, LGBTQ2S (Lesbian, Gay, Bisexual, Transgender, Queer, and Two-Spirit), and International communities in a student-led initiative with the mission to promote diversity, inclusion, and equity within the SoN. This coalition seeks to offer students who are often rendered invisible within the academy and society more broadly in relation to dominant cultures and normative expectations an opportunity to build relationships and expose shared histories of oppression in such a way that issues of social justice are uncovered. In response to nursing students and faculty who self-identify as members of dominant groups and who sought inclusion as allies, the leaders of the student community groups recognized a need to develop a position statement on allyship. The collaboration that transpired between the four groups of communities to develop the position statement led to the formation of the Student Equity Coalition. This article begins with the authors' definition of allyship, followed by a description of the context in which this unique initiative is taking place, the rationale behind developing a shared position statement on allyship, and the significance of this work in positioning and supporting nursing students of minority status as emerging nurse leaders.
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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 | 0.048 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".