Reconciling Taking the "Indian" out of the Nurse
Bibliographic record
Abstract
Currently, we are faced with an important equity gap and opportunity for nursing in higher education related to Indigenous Peoples and health. While Westernized higher education often marginalizes Indigenous Peoples, there is an important opportunity to respectfully engage with Indigenous Knowledges. Furthermore, broadening perspectives beyond a dominant Westernized worldview has the potential to advance higher education for Indigenous and non-Indigenous learners alike. We are concerned that ongoing assimilation of Indigenous learners poses a profound risk of social injustice that is contrary to the aim of higher education. In our effort to reconcile nursing education in this context, we offer this discussion paper of scholarly and grey literature interwoven with story work by Indigenous nursing students regarding their undergraduate experiences in the academy. Two significant interrelated gaps/opportunities are revealed: enactment of cultural safety and respectful engagement with Indigenous Knowledges. Action strategies include heart-mind knowledge connection, contextual learning, and two-way teaching and learning. It is our hope that this discussion will inspire critical conversations and meaningful action for educators to reconcile higher education and address structural racism. While reconciliation may be viewed as a duty in higher education and society, we further recognize it as a natural fit within the caring ethos of nursing.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.037 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.012 |
| 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".