On the validity of summative entrustment decisions
Bibliographic record
Abstract
Health care revolves around trust. Patients are often in a position that gives them no other choice than to trust the people taking care of them. Educational programs thus have the responsibility to develop physicians who can be trusted to deliver safe and effective care, ultimately making a final decision to entrust trainees to graduate to unsupervised practice. Such entrustment decisions deserve to be scrutinized for their validity. This end-of-training entrustment decision is arguably the most important one, although earlier entrustment decisions, for smaller units of professional practice, should also be scrutinized for their validity. Validity of entrustment decisions implies a defensible argument that can be analyzed in components that together support the decision. According to Kane, building a validity argument is a process designed to support inferences of scoring, generalization across observations, extrapolation to new instances, and implications of the decision. A lack of validity can be caused by inadequate evidence in terms of, according to Messick, content, response process, internal structure (coherence) and relationship to other variables, and in misinterpreted consequences. These two leading frameworks (Kane and Messick) in educational and psychological testing can be well applied to summative entrustment decision-making. The authors elaborate the types of questions that need to be answered to arrive at defensible, well-argued summative decisions regarding performance to provide a grounding for high-quality safe patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.347 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 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 teacher head, 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".