Introducing the Next Era in Assessment
Bibliographic record
Abstract
In this introduction, the guest editors of the "Next Era in Assessment" special collection frame the invited papers by envisioning a next era in assessment of medical education, based on ideas developed during a summit that convened professional and educational leaders and scholars. The authors posit that the next era of assessment will focus unambiguously on serving patients and the health of society, reflect its sociocultural context, and support learners' longitudinal growth and development. As such, assessment will be characterized as transformational, development-oriented and socially accountable. The authors introduce the papers in this special collection, which represent elements of a roadmap towards the next era in assessment by exploring several foundational considerations that will make the next era successful. These include the equally important issues of (1) focusing on accountability, trust and power in assessment, (2) addressing implementation and contextualization of assessment systems, (3) optimizing the use of technology in assessment, (4) establishing infrastructure for data sharing and data storage, (5) developing a vocabulary around emerging sources of assessment data, and (6) reconceptualizing validity around patient care and learner equity. Attending to these priority areas will help leaders create authentic assessment systems that are responsive to learners' and society's needs, while reaping the full promise of competency-based medical education (CBME) as well as emerging data science and artificial intelligence technologies.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".