“Languaging” tacit judgment in formal postgraduate assessment: the documentation of ad hoc and summative entrustment decisions
Bibliographic record
Abstract
While subjective judgment is recognized by the health professions education literature as important to assessment, it remains difficult to carve out a formally recognized role in assessment practices for personal experiences, gestalts, and gut feelings. Assessment tends to rely on documentary artefacts-like the forms, standards, and policies brought in under competency-based medical education, for example-to support accountability and fairness. But judgment is often tacit in nature and can be more challenging to surface in explicit (and particularly written) form. What is needed is a nuanced approach to the incorporation of judgment in assessment such that it is neither in danger of being suppressed by an overly rigorous insistence on documentation nor uncritically sanctioned by the defense that it resides in a black box and that we must simply trust the expertise of assessors. The concept of entrustment represents an attempt to effect such a balance within current competency frameworks by surfacing judgments about the degree of supervision learners need to care safely for patients. While there is relatively little published data about its implementation as yet, one readily manifest variation in the uptake of entrustment relates to the distinction between ad hoc and summative forms. The ways in which these forms are languaged, together with their intended purposes and guidelines for their use, point to directions for more focused empirical inquiry that can inform current and future uptake of entrustment in competency-based medical education and the responsible and meaningful inclusion of judgment in assessment more generally.
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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 | 0.238 | 0.607 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".