Moving Forward Together. Part 4: Advancing Physical Therapy Education for Indigenous Peoples’ Musculoskeletal Health
Bibliographic record
Abstract
SYNOPSIS: The Moving Forward Together series is a collective effort developed to inform, guide, and inspire musculoskeletal physical therapists to bring Indigenous health to the forefront of their work in clinical practice, research, and education and to strengthen their roles in allyship and advocacy for Indigenous Communities. In the fourth article of the “ Moving Forward Together” series, we highlight how physical therapy education can influence, and have responsibility for, improving students’ knowledge and skills to care for Indigenous Peoples who are living with musculoskeletal conditions. Through a collaborative process, our group—comprising Indigenous educators from Aotearoa/New Zealand, Australia, and Canada—has reflected on current initiatives, explored future directions, and offered suggestions on how physical therapists can contribute to positive change in Indigenous musculoskeletal education. An interconnected approach positions students to graduate as physical therapists, capable of demonstrating cultural humility to provide reflective, strengths-based, and relational, culturally safe health care to all people with musculoskeletal conditions. J Orthop Sports Phys Ther 2026;56(5):272-276. Epub 3 December 2025. doi:10.2519/jospt.2025.13740
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".