Entry-to-Practice Competency Expectations for Health Justice in Physiotherapy Curricula: A Scoping Review
Bibliographic record
Abstract
Purpose: Canadian physiotherapists are expected to demonstrate essential competencies upon entry to practice including health justice competencies. However, as an emerging topic among Canadian physiotherapy programmes, physiotherapy curricula may lack explicit content to develop skills related to health justice. This scoping review examined existing entry-level physiotherapy competencies related to health justice in Canada and countries other than Canada and evaluated how entry-level competencies related to health justice in Canadian physiotherapy practice compared to those of other countries. Method: Four databases (MEDLINE, Emcare, Embase, and CINHL) and the grey literature were searched. Results: Four thousand three hundred seventy-seven relevant abstracts and 71 grey literature sources were identified respectively. One hundred seven sources underwent full text review, with 12 database articles and 13 grey literature sources selected for data extraction. None of the included articles specifically articulated one or more competencies for health justice; instead competencies in content areas relevant to health justice were identified. During the data extraction phase four themes were identified: (1) lack of specificity, clarity, and consistency, which was further separated into two subthemes (a) lack of consistency and clarity of definitions and concepts (b) lack of an assessment tool; (2) author identification; (3) curriculum development; and (4) experiential learning. Limitations include restricting the search to English language only, and grey literature limited to specific PDFs and websites. Conclusions: The data collected in this scoping review demonstrates gaps in the integration of health justice in Canadian and international entry-level physiotherapy curricula.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".