Identifying Relevant Content to Inform a Comprehensive Indigenous Health Curriculum: A Scoping Review
Bibliographic record
Abstract
Purpose: To identify the entry-level curricular content related to Indigenous health recommended for entry-level physiotherapy (PT) programs in Canada and other similar countries. Methods: , and their derivatives. Grey literature sources were hand searched and included Canadian PT professional documents, PT Program websites, Truth and Reconciliation Commission (TRC) sources, and a Google search. Data related to curriculum characteristics, methods of delivery, and barriers and facilitators to implementation were extracted from relevant references. Stakeholders reviewed study findings. Results: Forty-five documents were included. Documents focused on Indigenous peoples in Canada, Aboriginal and Torres Strait Islanders in Australia, and Māori in New Zealand. Canadian PT programs appeared to rely on passive teaching methods while programs in Australia and New Zealand emphasized the importance of partnering and engaging with Indigenous people. Barriers to incorporating indigenous health curriculum included an overcrowded curriculum and difficulty establishing relevance of Indigenous content (i.e., meaning). Conclusions: Similarities and differences were found between curricula content and approaches to teaching IH in Canada and the other countries reviewed. Strategies to promote greater engagement of Indigenous people in the development and teaching of IH is recommended.
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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 | 0.018 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".