The Feasibility and Acceptability of Using a Portfolio to Assess Professional Competence
Bibliographic record
Abstract
PURPOSE: Little is known about physical therapists' views on the use of portfolios to evaluate professional competence. The purpose of this study was to gather the opinions of physical therapists on the feasibility and acceptability of a portfolio prepared to demonstrate evidence of clinical specialization through reported activities and accomplishments related to professional development, leadership, and research. METHODS: Twenty-nine Canadian physical therapists practising in the neurosciences area were given 8 weeks to prepare a professional portfolio. Participants submitted the portfolio along with a survey addressing the preparation of the portfolio and its role as an assessment tool. Qualitative content analysis was used to interpret the participants' comments. RESULTS: Participants reported that maintaining organized records facilitated the preparation of their portfolio. They experienced pride when reviewing their completed portfolios, which summarized their professional activities and highlighted their achievements. Concerns were noted about the veracity of self-reported records and the ability of the documentation to provide a comprehensive view of the full scope of the professional competencies required for clinical specialization (e.g., clinical skills). CONCLUSION: The study's findings support the feasibility and acceptability of a portfolio review to assess professional competence and clinical specialization in physical therapy and have implications for both physical therapists and professional agencies.
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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 | 0.068 | 0.208 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".