Design and Implementation of a National Program of Assessment Model – Integrating Entrustable Professional Activity Assessments in Canadian Specialist Postgraduate Medical Education
Bibliographic record
Abstract
Traditional approaches to assessment in health professions education systems, which have generally focused on the summative function of assessment through the development and episodic use of individual high-stakes examinations, may no longer be appropriate in an era of competency based medical education. Contemporary assessment programs should not only ensure collection of high-quality performance data to support robust decision-making on learners' achievement and competence development but also facilitate the provision of meaningful feedback to learners to support reflective practice and performance improvement. Programmatic assessment is a specific approach to designing assessment systems through the intentional selection and combination of a variety of assessment methods and activities embedded within an educational framework to simultaneously optimize the decision-making and learning function of assessment. It is a core component of competency based medical education and is aligned with the goals of promoting assessment for learning and coaching learners to achieve predefined levels of competence. In Canada, postgraduate specialist medical education has undergone a transformative change to a competency based model centred around entrustable professional activities (EPAs). In this paper, we describe and reflect on the large scale, national implementation of a program of assessment model designed to guide learning and ensure that robust data is collected to support defensible decisions about EPA achievement and progress through training. Reflecting on the design and implications of this assessment system may help others who want to incorporate a competency based approach in their own country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".