Meeting Together in a Good Way: A Discussion on the Educational Mapping Activity of Indigenous Content in a Nurse Practitioner Program
Bibliographic record
Abstract
A Primary Health Care Nurse Practitioner program carried out a unique curriculum mapping activity related to Indigenous content which resulted in the creation of a report entitled Maawanji'idiwag: Meeting Together in a Good Way. The activity was carried out by a working group which consisted of Indigenous knowledge keepers, Elders, faculty, and a student. The curriculum mapping activity prioritized the health-related Calls to Action from the Final Report of the Truth and Reconciliation of Canada (TRC), the Calls to Justice from Reclaiming Power and Place: The Final Report of the National Inquiry into Missing and Murdered Indigenous Women and Girls and the articles from the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) as ‘Action Calls’ within a Nurse Practitioner Program. The mapping process was also guided by Bartlett’s decolonizing research framework The associated literature with the intersecting concepts of decolonization, Indigenization, reconciliation, cultural safety, and humility were linked to these national reports and the curriculum mapping activity and subsequent report. The goal of this paper is to share the many insights and learning from the curriculum mapping report to promote further deep dialogues, involvement and meaningful change within nursing education and health care.
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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 | 0.071 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.112 | 0.038 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.023 | 0.042 |
| Insufficient payload (model declined to judge) | 0.005 | 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".