The CanMEDS portfolio: a tool for reflection in a fellowship programme
Bibliographic record
Abstract
BACKGROUND: We developed a portfolio framed by the CanMEDS roles for use in a paediatric fellowship programme. The portfolio includes four components: a record of participation and achievement; career goals and professional development; physical evidence; and reflective writing. METHODS: Once the portfolio was in use for 6 months, we studied how fellows and faculty members were using the portfolio, and what they found to be its advantages and disadvantages. RESULTS: Fellows reported that it kept them organised and assisted them in setting their goals. They appreciated having a central place to record their accomplishments, as this allowed them to keep a thorough curriculum vitae. The portfolio was helpful in giving them the opportunity to honestly reflect on their achievements and setbacks, and, after reviewing this in their own minds, they were in a strong position to set an agenda for their meetings with supervisors. Both the fellows and supervisors were in agreement that the portfolio led to improved discussions at their meetings. Both groups also reported that this new tool was useful in furthering the career development of trainees, which was one of the main goals in its inception. Faculty supervisors also had a stronger sense of the fellows' work, and also of any gaps in training, as a result of using this tool. DISCUSSION: We hope that this reflection tool will be adapted for use in other training programmes. If it is introduced elsewhere, we would recommend that learners and staff receive ample training in its use so that it can be maximally effective.
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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 | 0.044 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".