Managing assessment during curriculum change: Ottawa Consensus Statement
Bibliographic record
Abstract
Curriculum change is relatively frequent in health professional education. Formal, planned curriculum review must be conducted periodically to incorporate new knowledge and skills, changing teaching and learning methods or changing roles and expectations of graduates. Unplanned curriculum evolution arguably happens continually, usually taking the form of "minor" changes that in combination over time may produce a substantially different programme. However, reviewing assessment practices is less likely to be a major consideration during curriculum change, overlooking the potential for unintended consequences for learning. This includes potentially undermining or negating the impact of even well-designed and important curriculum changes. Changes to any component of the curriculum "ecosystem "- graduate outcomes, content, delivery or assessment of learning - should trigger an automatic review of the whole ecosystem to maintain constructive alignment. Consideration of potential impact on assessment is essential to support curriculum change. Powerful contextual drivers of a curriculum include national examinations and programme accreditation, so each assessment programme sits within its own external context. Internal drivers are also important, such as adoption of new learning technologies and learning preferences of students and faculty. Achieving optimal and sustainable outcomes from a curriculum review requires strong governance and support, stakeholder engagement, curriculum and assessment expertise and internal quality assurance processes. This consensus paper provides guidance on managing assessment during curriculum change, building on evidence and the contributions of previous consensus papers.
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 | 0.327 | 0.382 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.019 | 0.018 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".