Implementation of competency-based medical education in a Canadian medical oncology training program: Lessons from our first year.
Bibliographic record
Abstract
10514 Background: As part of a university wide initiative, CBME was implemented in our MO training program in July 2017. Stages, Entrustable Professional Activity (EPA) assessments and Required Training Experiences established by the Royal College of Physicians and Surgeons of Canada were adopted. MedTech Central, the electronic portfolio developed at our university was used for assessment collection. We share here observations and experiences from our first year of implementation. Methods: Assessment metrics were obtained through MEdTech. Ethics was granted by Queen’s University as part of an ongoing research study on feedback. Lessons learned were compiled from discussions between the Program Director, Residents, Program Administrator, CBME Education Consultant and CBME lead. Results: A total of 195 assessments were completed July 2017-November 2018. 81% were EPA assessments and the remainder multisource feedback, rubrics and field notes. The median number of assessments per faculty was 17 (0-42). 52% of assessments included written “Comments” or “Next steps”. A median of 6 assessments per faculty member included specific or actionable feedback. Lessons learned centered on: 1) Faculty and Resident development and engagement (critical before, during and after implementation); 2) Value of sharing work of CBME (CBME Education Consultant, CBME Lead, Academic Advisors, Competence Committee); 3) Importance of effective communication strategy with stakeholders 4) Importance of collaboration with other training programs at institutional and national levels; 5) Culture change (a slow process); 6) Resident concerns regarding lack of global assessment; 7) Assessment plan challenges (How many observations required?); 8) Burden of CBME (Resident driven assessments or a better balance?) ; 9) Limitations of e-portfolio (How to live track and by whom?); 10) Costs 11) Value of continuous quality assurance and improvement. Conclusions: Our first year of implementation was successful in introducing CBME concepts, work based assessments and e-portfolios. Ongoing work is needed, including increasing the number of assessments and quality of feedback.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".