An Environmental Scan of Indigenous Cultural Safety in Canadian Baccalaureate Nursing and Midwifery Programs
Bibliographic record
Abstract
BACKGROUND: The Truth and Reconciliation Commission (TRC) TRC has called to increase the number of Indigenous practitioners and include cultural competency education in their curricula. However, it remains unknown how nursing and midwifery programs are progressing towards these goals. PURPOSE: To examine the extent to which baccalaureate nursing and midwifery programs are creating culturally safe spaces for Indigenous students, responding to TRC-recommended curricular changes, and including Indigenous content. METHODS: A digital environmental scan of accredited baccalaureate nursing and midwifery programs in Canada was conducted. Analysis was conducted using descriptive statistics. RESULTS: Of the 107 programs, less than one-fifth (n = 19, 17.8%) met all three cultural safety criteria. More than half (n = 59, 55.1%) included culturally safe spaces for Indigenous students, 20 (18.7%) satisfied TRC call #24 to require Indigenous-relevant coursework, and one-third (n = 36, 33.6%) were seen as infusing their curricula with Indigenous-related content. CONCLUSIONS: This represents the first attempt to systematically catalog nursing and midwifery programs' response to the TRC Calls to Action. Most schools have not made substantial progress towards cultural safety. Nursing and midwifery programs should commit to expanding their cultural safety programming to incorporate multiple ways of knowing and being in their curricula.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".