Academic Allyship in Nursing: Deconstructing a Successful Community-Academic Collaboration
Bibliographic record
Abstract
Public health and social care systems in Canada are frequently racist and discriminatory towards Indigenous people and exacerbates health inequities that Indigenous people experience. In New Brunswick, there are a range of culturally informed health and social services being offered within First Nations communities and by Indigenous organization that operate outside of reserves. Some of these services and organizations rely on support from non-Indigenous allies to meet the needs of their community members. However, it can be challenging for non-Indigenous people to engage in allyship due to unconscious bias, false assumptions, and lack of cross-cultural understanding. Effective allyship can also be challenging due a lack of understanding of the time, resources, and commitments that are required. Academic allyship from within post-secondary institutions can be particularly challenging because of a history of past harm done to Indigenous communities and entrenched colonial structures and policies. The purpose of this article is to provide an example of academic allyship with an urban Indigenous organization and consider some of the success factors that have supported this ongoing collaboration. The authors reflect on more than four years of successful collaboration and use a recent project to illustrate what worked and why. The success factors were, building a relationship and trust; becoming better informed; offering support freely; stepping off the beaten path (to tenure); staying critically self-aware; and enjoying the work (immensely). The success factors are not intended as a roadmap because every collaboration is unique. However, they may help potential allies enter potential collaboration being better informed. Academic allyship can be highly impactful and highly rewarding, but it also should not be undertaken without reflection on one’s reasons for doing so and capacity to commit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.059 | 0.104 |
| Scholarly communication | 0.038 | 0.018 |
| Open science | 0.006 | 0.050 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".