The creation of a pediatric residency portfolio using the CanMEDS format
Bibliographic record
Abstract
Portfolios in medical education have seen a growth in the last few years with the onset of competency based assessment. The Royal College of Physicians and Surgeons of Canada have adopted the CanMeds roles to represent the various domains in which a physician should be competent. Residency programs have struggled with valid assessment tools for these various roles. The professionalism literature points to the need for ways to help the trainee develop self-reflective and self-assessment skills as these are needed by the practicing physician. Portfolios provide a flexible, multifaceted means of collecting evidence of achievement of competencies over time. We wish to develop and evaluate the use of portfolios in Memorial’s four year Pediatric residency program. All present pediatric residents will be surveyed to determine their selfassessment of their level of competence in each of the CanMeds roles. We will ask questions regarding their insight about their own level of professionalism in the workplace. We aim to develop a web-based resident portfolio in the CanMeds format. This portfolio would be flexible but deliberately designed to fit the activities and expectations of the Pediatric residency program. It will be structured in that procedures and evaluations can be captured but also with the ability to contain resident reflections and supervisor comments on resident progress. Following one year of use of the portfolios, self-assessment and acceptability of the portfolio will be reassessed. We hope this tool will contribute to resident insight.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".