Professional portfolios used by Canadian occupational therapists: How can they be improved?
Bibliographic record
Abstract
Professional portfolios are widely used in continuing professional development (CPD), despite limited evidence of their effectiveness for improving practice and professional competence. Occupational therapy regulatory organizations in Canada have implemented professional portfolios as tools that support engagement in CPD. To advance research and practice on the use of portfolios, we conducted a critical analysis of their format, content, and embedded learning process. This paper aims to describe and compare the portfolios’ characteristics when they are used as a tool to facilitate engagement in CPD. A document review approach was used to analyze documents describing continuing competence programs and portfolios and to compare their characteristics. Data was retrieved from documents using a coding scheme and content was compared to the literature. In Canada, seven out of 10 regulatory organizations have implemented a portfolio. They are similar in their content and proposed self-directed learning approach. Their strength is that they all promote self-assessment, reflection, and development of a CPD plan. However, the tools provided can be improved to help engage in more genuine reflection and integration of learning into practice. Our review of the content, tools, and proposed learning process of portfolios revealed avenues for improvement and future research.
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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 | 0.037 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".