Entry-To-Practice Competency Expectations for Health Justice in Canadian Physiotherapy Curricula: A Scoping Review
Bibliographic record
Abstract
In Canada, physiotherapists are expected to possess and demonstrate a number of essential competencies upon entry-to-practice. Despite the inclusion of concepts related to health justice, inclusivity, and cultural awareness into the Physiotherapy Accreditation Standards for Canadian physiotherapy programs, health justice is an emerging topic among Canadian physiotherapy programs and current curricula may be lacking explicit health justice frameworks. Due to the importance of health justice in the context of Canadian physiotherapy education and practice, further research is required to examine how health justice has been integrated into entry-level curricula to date and to identify opportunities for improvement. Thus, the primary objective of this paper is to examine existing Canadian entry-level competencies for physiotherapy related to health justice. The secondary objective is to examine the themes of current recommendations and written resources related to health justice for entry-level physiotherapists in countries other than Canada that have already taken steps to incorporate principles of health justice into their health program curricula. The final objective is to evaluate how existing entry-level competencies related to health justice in Canadian physiotherapy practice compare to those of other countries.
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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 | 0.041 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".