Factors predicting competence as assessed with the written component of the Canadian Physiotherapy Competency Examination
Bibliographic record
Abstract
Little is known about the predictors of success on the written component of the Physiotherapy Competency Examination (PCE), the requirement for licensure in most Canadian jurisdictions. The purpose of this study was to examine the relationship between educational factors and the performance of Canadian educated physical therapists (CEPTs) and internationally educated physical therapists (IEPTs). An anonymized database composed of 24 sittings of the examination from the years 2001 to 2004 was used. Pearson correlation analyses and regression analyses were conducted to examine the relationships between educational factors and scores. ANOVA was used to compare differences in scores between candidate groups. CEPTs, first-time writers, and candidates writing in their year of graduation had the highest pass rates. The performance of both CEPTS and IEPTs does not appear to decline for any candidate writing beyond the first year post-graduation. The novel finding that the performance of candidates did not decline with increasing years postgraduation warrants further study. Other future research initiatives should include additional demographic and educational factors and address the relationship between performance on both components of the PCE and actual clinical practice.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".