Physiotherapy Students’ Performance in Clinical Education: An Analysis of 1 Year of Canadian Cross-Sectional Data
Bibliographic record
Abstract
Purpose: This research examines 1 year of cross-sectional, Canada-wide ratings from clinical instructors using the Canadian Physiotherapy Assessment of Clinical Performance (ACP) and analyzes the performance profiles of physiotherapy students’ performance ratings over the course of their entry-to-practice clinical placements. Method: Canadian physiotherapy programmes that use the ACP were invited to submit anonymized, cross-sectional data for placements completed during 2018. Descriptive analyses and summary statistics were completed. Mixed-effects modelling was used to create typical performance profiles for each evaluation criterion in the ACP. Stepwise ordered logistic regression was also completed. Results: Ten programmes contributed data on 3,290 placements. Profiles were generated for each ACP evaluative item by means of mixed-effects modelling; three profiles are presented. In all cases, the predicted typical performance by the end of 24 months of study was approximately the rating corresponding to entry level. Subtle differences among profiles were identified, including the rate at which a student may be predicted to receive a rating of “entry level.” Conclusions: This analysis identified that, in 2018, the majority of Canadian physiotherapy students were successful on clinical placements and typically achieved a rating of “entry level” on ACP items at the end of 24 months.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".