Performance assessment: Consensus statement and recommendations from the 2020 Ottawa Conference
Bibliographic record
Abstract
Introduction In 2011 the Consensus Statement on Performance Assessment was published in Medical Teacher. That paper was commissioned by AMEE (Association for Medical Education in Europe) as part of the series of Consensus Statements following the 2010 Ottawa Conference. In 2019, it was recommended that a working group be reconvened to review and consider developments in performance assessment since the 2011 publication.Methods Following review of the original recommendations in the 2011 paper and shifts in the field across the past 10 years, the group identified areas of consensus and yet to be resolved issues for performance assessment.Results and discussion This paper addresses developments in performance assessment since 2011, reiterates relevant aspects of the 2011 paper, and summarises contemporary best practice recommendations for OSCEs and WBAs, fit-for-purpose methods for performance assessment in the health professions.
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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 | 0.412 | 0.390 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.020 | 0.020 |
| Research integrity | 0.021 | 0.035 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".