The role of ePortfolios in supporting continuing professional development in practice
Bibliographic record
Abstract
ePortfolios, based on models of reflective practice, are viewed as important tools in facilitating and supporting lifelong learning across the medical education continuum. MAINPORT, the ePortfolio designed by the Royal College of Physicians and Surgeons of Canada, supports the continuing professional development (CPD) and lifelong learning of specialist physicians practicing in Canada by providing tools to develop CPD plans, set and track progress of established learning goals, document and reflect on learning activities, and create the foundation for physicians to manage their learning. In this article, the authors summarize the key design principles of the Royal College's ePortfolio: learner-centered; interoperable; ease of access. The current core functionality as well as future planned functionality for MAINPORT are described under three domains: recording and reflecting on completed CPD activities; managing learning in practice; accessing learning resources and programs. The future MAINPORT will evolve to become a foundational tool to support the shift towards competency-based medical education across the continuum of medical education; from residency to retirement. MAINPORT will facilitate the ability of physicians to demonstrate their expertise over time and how their learning has enabled improvements to their practice in contributing to improved health outcomes for patients.
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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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".