Assessment for Learning: The University of Toronto Temerty Faculty of Medicine M.D. Program Experience
Bibliographic record
Abstract
(1) Background: Programmatic assessment optimizes the coaching, learning, and decision-making functions of assessment. It utilizes multiple data points, fit for purpose, which on their own guide learning, but taken together form the basis of holistic decision making. While they are agreed on principles, implementation varies according to context. (2) Context: The University of Toronto MD program implemented programmatic assessment as part of a major curriculum renewal. (3) Design and implementation: This paper, structured around best practices in programmatic assessment, describes the implementation of the University of Toronto MD program, one of Canada’s largest. The case study illustrates the components of the programmatic assessment framework, tracking and making sense of data, how academic decisions are made, and how data guide coaching and tailored support and learning plans for learners. (4) Lessons learned: Key implementation lessons are discussed, including the role of context, resources, alignment with curriculum renewal, and the role of faculty development and program evaluation. (5) Conclusions: Large-scale programmatic assessment implementation is resource intensive and requires commitment both initially and on a sustained basis, requiring ongoing improvement and steadfast championing of the cause of optimally leveraging the learning function of assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".