The Development of the Canadian Physiotherapy Assessment of Clinical Performance (ACP) 2.0 – Alignment with the 2017 Competency Profile
Bibliographic record
Abstract
Purpose: Clinical education and assessment of students' performance during clinical placements are key components of Canadian entry-to-practice physiotherapy curriculum and important in developing entry-level physiotherapy practitioners. The Canadian Physiotherapy Assessment of Clinical Performance (ACP) is the measure currently used to assess physiotherapy student performance on clinical placements in most of the entry-to-practice physiotherapy programmes across Canada. The release of the 2017 Competency Profile by the National Physiotherapy Advisory Group resulted in a revision of the existing ACP. The purpose of this study is to report the process used to develop a revised version of the ACP based on the 2017 Competency Profile, henceforth called the ACP 2.0. Method: Using a multistage process, we sought input from Canadian clinical education academics, an expert consultant panel, as well as physiotherapists across Canada using a questionnaire, meetings, and an online survey, respectively. Results: = 12) met three times. There were 144 physiotherapists who initiated the national, online, survey and met the inclusion criteria; 84 completed the survey. In the ACP 2.0, rating scales and comments boxes were grouped, and additional text was added to 12 items for further clarification. The ACP 2.0 came to have 18 items and 9 comment boxes in addition to summative comments, in contrast to the original ACP's 21 items and 9 comment boxes. Conclusions: In November 2020, Canadian clinical education academics reviewed the proposed draft ACP 2.0 and unanimously accepted it for implementation in Canadian physiotherapy university 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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".