Build Insight, Change Thinking, Inform Action: Considerations for Increasing the Number of Indigenous Students in Canadian Physical Therapy Programmes
Bibliographic record
Abstract
Purpose: We explored the perspectives of experts on increasing the recruitment of Indigenous students into Canadian physical therapy (PT) programmes. Methods: For this qualitative interpretivist study, we conducted in-depth, semi-structured interviews with individuals with expertise in encouraging Indigenous students to pursue higher education, recruiting them into PT programmes, or both. Data were organized using NVivo and analyzed using the DEPICT method, which included inductive and deductive coding to develop broader themes. Results: Analyzing the participants’ perspectives revealed three themes, which could be layered sequentially, so that each informed the next: (1) building insight by increasing awareness of structural forces and barriers; (2) changing thinking, using a paradigm shift, from the dominant Eurocentric orientation to a view that respects the sovereignty and self-determination of Indigenous peoples; and (3) informing action by recommending practical strategies to facilitate the recruitment of Indigenous students into Canadian PT programmes. Conclusions: This is the first study to provide evidence of the structural considerations, barriers to, and facilitators of increasing the recruitment of Indigenous students into Canadian PT programmes.
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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 | 0.064 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.047 | 0.034 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".