Methodological approaches for identifying competencies for the physiotherapy profession: a scoping review
Bibliographic record
Abstract
Abstract Physiotherapy competencies inform the education and regulation of the profession. Many different methods appear to be used to identify competencies and there is no consensus on optimal methods to identify competencies. The purpose of this review is to synthesize the methodological approaches used to identify competencies for the physiotherapy profession and summarize the nature of those competencies. We searched MEDLINE, EMBASE, CINAHL, and the grey literature from inception to June 2020. Two independent reviewers screened for empirical peer-reviewed articles that aimed to identify professional physiotherapy competencies. General study characteristics, competency characteristics (e.g., target practice area), and methodological characteristics (e.g., study population, data collection and analysis method for each methodological step) were extracted. Descriptive statistics and narrative synthesis were performed. Of the 9529 references screened, 38 articles describing 35 studies published between 1980 and 2020 were included. Orthopaedics (20.0%) was the most commonly targeted area of practice. Studies used one to eight methodological steps whose objective was to generate (16 studies), validate (18 studies), assign value (21 studies), refine (10 studies), or triangulate (3 studies) competencies, or to address multiple objectives (10 studies). The most commonly used methods were surveys to assign value (n = 20, 95%), and group techniques to refine competencies (n = 7, 70%). Physiotherapists with experience in the area of competence was the most commonly consulted stakeholder group (80% of studies). This review can provide methodological guidance to stakeholders such as educators and regulators that aim to identify professional competencies in the future.
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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 | 0.265 | 0.477 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.080 | 0.054 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".