Validity theory applied to entrustment as an approach to assessment
Bibliographic record
Abstract
In adopting entrustment-based assessments, the construct has shifted from assessing learners’ capability to provide competent care to their readiness for the responsibility for the welfare of patients and permission to perform clinical care with appropriate autonomy. Competence committees charged with making entrustment-based decisions must make decisions that are valid, fit for purpose, and interpreted appropriately. However, entrustment as a construct is complex and warrants a discussion regarding its relation to validity. While many different validity questions may be asked in the context of entrustable professional activities (EPAs), this chapter focuses on what we believe is the most salient and novel feature of EPA-based programs, which is the introduction of entrustment decision-making as an approach to assessment of health professionals in training. Validity theory, with reference to the models of Messick and Kane, is discussed in the context of entrustment. This leads to reflections on how some assumptions regarding validity may need to be reconceptualized, how sources of evidence and validity arguments can support defensible decisions, and how threats to validity must be considered and minimized.
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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 | 0.112 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.004 | 0.055 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".