Using the portfolio to foster reflective practice through formative assessment of the CanMEDS professional role: Preparing residents for the competent-to-expert milestone
Bibliographic record
Abstract
Background/Objectives The transition from competent to expert practitioner is a key milestone in the first years after completing residency. Successful consultants practice life-long reflection of their practice in all CanMEDS domains. Residency programs should guide and motivate residents to develop into “reflective practitioners”. Methods A portfolio was introduced to the Anesthesia Residency Program at MUN during the 2011/12 academic year for this purpose. A key component of the portfolio is a reflection on the CanMEDS Professional role. Prior to this, an education specialist held a workshop to introduce residents to narrative reflection. The portfolio reflection follows several steps. First, they complete assigned background reading. Then they write a narrative summary of a clinical, teaching, research or other experience which impacted their thinking about professionalism. A designated faculty member guides residents to reflect on their experience, demonstrate what they did well and what they would do differently in the future. The faculty member gives formative feedback on the portfolio and completes a qualitative assessment. The program director reviews portfolios once a year. A resident interested in reflective practice acts as a resource for residents with questions, helps them initiate portfolio writing and facilitates discussion of the portfolio project among the resident group. Review and feedback are used to reflect on and improve the portfolio process. Results Feedback from residents, the resident mentor and the faculty mentor has been positive to date. Conclusions The plan is to add a new CanMEDS role each year for the development of resident portfolios as they move from PGY1-5.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".