Exploring Record Keeping, Clinical Reasoning, and Practice Context: Peer Assessment Findings from the Perspective of Situational Competence
Bibliographic record
Abstract
Purpose: The College of Physiotherapists of Ontario developed its peer practice assessment (PA) process on the basis of the statutory requirements for quality assurance. We previously reported outcomes from physiotherapists who had two PAs. The aims of the current research were to identify areas of sub-optimal performance on the assessments and explore any associations with the physiotherapists' practice context. Methods: We examined scores from the PAs of all physiotherapists who had two unrelated PAs between 2004 and 2012 (n=117), and we examined assessment reports for those who had specific patterns of sub-optimal outcomes (n=22). We conducted qualitative content analysis on the assessment reports to identify areas deemed to need improvement and the contexts in which the physiotherapists practised. Comparisons of proportions were carried out using Fisher's exact test. Results: The most common area of sub-optimal scores was record keeping, followed by clinical reasoning as assessed by means of chart-stimulated recall. Record-keeping deficits were related to either clinical care or administrative requirements (e.g., documenting patient consent in the manner required by law). At the second PA, record-keeping deficits were predominantly administrative. Physiotherapists with sub-optimal outcomes disproportionately worked in private practice contexts (p=0.026). Conclusions: Ontario physiotherapists generally maintain high-quality practice. Regulatory bodies may consider developing support strategies for meeting professional standards that take practice context into account.
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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 | 0.020 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".