Methodological approaches for identifying competencies for the physiotherapy profession: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it