Methods and Processes for First Nations Health Curriculum Development for Nursing, Medicine, Dentistry and Allied Health Entry-Level Programs: A Scoping Review
Bibliographic record
Abstract
The inclusion of First Nations health curricula in programs is critical for the development of culturally safe graduates, however, less is known about how to embed content into curriculum in ways that reflect best practice and pedagogy. The aim of this scoping review was to describe methods and processes of First Nations health curriculum development in nursing, medical, dentistry, and allied health entry-level programs in international peer-reviewed journals. Systematic searches of databases were completed including CINAHL, Proquest, Medline, and Informit; with additional searches in Google Scholar and First Nations-led journals. A total of 104 articles met inclusion criteria; the majority relating to medicine ( n = 38) and nursing/midwifery ( n = 17) student cohorts. Methods and processes for embedding First Nations health content are described, including First Nations-led development and co-leadership, resulting in a suggested model for curriculum development. Evidence-informed curriculum development is critical to ensure effective methods and processes are adopted and cultural safety learning outcomes are achieved.
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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 | 0.063 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".