The Next Era of Assessment Within Medical Education: Exploring Intersections of Context and Implementation
Bibliographic record
Abstract
In competency-based medical education (CBME), which is being embraced globally, the patient-learner-educator encounter occurs in a highly complex context which contributes to a wide range of assessment outcomes. Current and historical barriers to considering context in assessment include the existing post-positivist epistemological stance that values objectivity and validity evidence over the variability introduced by context. This is most evident in standardized testing. While always critical to medical education the impact of context on assessment is becoming more pronounced as many aspects of training diversify. This diversity includes an expanding interest beyond individual trainee competence to include the interdependency and collective nature of clinical competence and the growing awareness that medical education needs to be co-produced among a wider group of stakeholders. In this Eye Opener, we wish to consider: 1) How might we best account for the influence of context in the clinical competence assessment of individuals in medical education? and by doing so, 2) How could we usher in the next era of assessment that improves our ability to meet the dynamic needs of society and all its stakeholders? The purpose of this Eye Opener is thus two-fold. First, we conceptualize - from a variety of viewpoints, how we might address context in assessment of competence at the level of the individual learner. Second, we present recommendations that address how to approach implementation of a more contextualized competence assessment.
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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 | 0.056 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.056 |
| Scholarly communication | 0.028 | 0.037 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".