Assessment for Learning: The University of Toronto Temerty Faculty of Medicine M.D. Program Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it