Development of Physical Therapy Practical Assessment System by Using Multisource Feedback
Bibliographic record
Abstract
The purposes of the research were (1) to develop the physical therapy practical assessment system by using the multisource feedback (MSF) approach and (2) to investigate the effectiveness of the implementation of the developed physical therapy practical assessment system. The development of physical therapy practical assessment system by using MSF was determined by nine experts in physical therapy. Suitability and feasibility of the system for each sub-group were investigated. Five input factors, two process factors, two output factors, and two feedback factors were involved in the system. Level of suitability and feasibility of elements in each sub-group presented at high to the highest level. In system testing, 40 physical therapy students were participated. Raters consisted of clinical educators, students (self-assessment), friends (students who in the same practical group), and patients. Twice assessments during the period of clinical practice were performed. Data analysis for generalizability coefficient (G-coefficient) was evaluated by the EduG program. Quality of the system was evaluated 4 aspects including the utility, feasibility, propriety, and accuracy. These were calculated by mean () and standard deviation (SD). The values of G-coefficient for absolute and relative were 0.86 and 0.88, respectively. In addition, quality of the system showed value at high to the highest level in all aspects.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".