Observations and Performances “with distinction” by Physical Therapy Students in Clinical Education: Analysis of Checkboxes on the Physical Therapist Clinical Performance Instrument (PT-CPI) over a 4-Year Period
Bibliographic record
Abstract
PURPOSE: To describe how often the 24 performance criteria of the Physical Therapist Clinical Performance Instrument (PT-CPI) were not observed and how often they were rated exceptionally well for physical therapy (PT) students in relation to clinical placement descriptors. METHODS: Indicators of "not observed," performance "with distinction," and "significant concerns" were tabulated from 1,460 clinical placements between 2008 and 2012. The rates for these indicators were evaluated with respect to catchment area, practice setting (hospital/institutional or community-based), practice area (musculoskeletal, cardiorespiratory, neurology, paediatrics, geriatrics, or variety), and level (junior to senior). RESULTS: Of the 24 PT-CPI criteria, 15 had observation rates >95%. Of the other nine criteria, some showed significant differences in observation rates across level, practice setting, and practice area. Ratings of "with distinction" were awarded most often for criteria related to professionalism and communication and were awarded more often in community-based settings than in hospital/institutional settings. For some criteria, "with distinction" was awarded more often in paediatrics placements than in other areas. The "significant concerns" checkboxes were rarely used. CONCLUSIONS: The overall observation rates were very similar to those reported elsewhere. The findings related to performance "with distinction" and observation rates relative to setting and practice area are new contributions to physical therapy knowledge.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".