A “two-eyed seeing” approach to Indigenizing nursing curricula
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Educational institutions, including schools of nursing, find themselves in significant times, as they work to Indigenize programs, and strive to repair and heal relationships with Indigenous peoples as recommended in the Truth and Reconciliation Commission of Canada (2015). Educators question where to begin the process, how such Indigenization should occur, and what the curricular end result should look like. In response, the authors considered many aspects from the literature, specific to nursing programs. The following themes were explored: partnering with community, cultural relevance, and faculty development. Through the utilization of a “two-eyed seeing” approach, institutional administrators need to partner with Indigenous Elders and community members to facilitate relationships required to provide the knowledge necessary to bring about change within educational programs. It is through such an approach that nursing curricula can be designed to be culturally safe and relevant for both Indigenous and non-Indigenous learners, and faculty can be supported in their growth and development in Indigenous knowledge. The authors propose that through “two-eyed seeing” and the integration of the Aboriginal Nurses Association of Canada (2009) core competencies, Indigenization of nursing curricula may ultimately move forward in a culturally reciprocal and respectful way.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it