Synthesis and perspectives from the Ottawa 2022 conference on the assessment of competence
Bibliographic record
Abstract
INTRODUCTION: The Ottawa Conference on the Assessment of Competence in Medicine and the Healthcare Professions was first convened in 1985 in Ottawa. Since then, what has become known as the Ottawa conference has been held in various locations around the world every 2 years. It has become an important conference for the community of assessment - including researchers, educators, administrators and leaders - to share contemporary knowledge and develop international standards for assessment in medical and health professions education. METHODS: The Ottawa 2022 conference was held in Lyon, France, in conjunction with the AMEE 2022 conference. A diverse group of international assessment experts were invited to present a symposium at the AMEE conference to summarise key concepts from the Ottawa conference. This paper was developed from that symposium. RESULTS AND DISCUSSION: This paper summarises key themes and issues that emerged from the Ottawa 2022 conference. It highlights the importance of the consensus statements and discusses challenges for assessment such as issues of equity, diversity, and inclusion, shifts in emphasis to systems of assessment, implications of 'big data' and analytics, and challenges to ensure published research and practice are based on contemporary theories and concepts.
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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 | 0.136 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.031 | 0.013 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".