The critical role of direct observation in entrustment decisions
Bibliographic record
Abstract
Background: Entrustment decisions may be retrospective (based on past experiences with a trainee) or real-time (based on direct observation). We investigated judgments of entrustment based on assessor prior knowledge of candidates and based on systematic direct observation, conducted in an objective structured clinical exam (OSCE). Methods: Sixteen faculty examiners provided 287 retrospective and real-time entrustment ratings of 16 cardiology trainees during OSCE stations in 2019 and 2020. Reliability and validity of these ratings were assessed by comparing correlations across stations as a measure of reliability, differences across postgraduate years as an index of construct validity, correlation to standardized in-training exam (ITE) as a measure of criterion validity, and reclassification of entrustment as a measure of consequential validity. Results: Both retrospective and real-time assessments were highly reliable (all intra-class correlations >0.86). Both increased with year of postgraduate training. Real-time entrustment ratings were significantly correlated with standardized ITE scores; retrospective ratings were not. Real-time ratings explained 37% (2019) and 46% (2020) of variance in examination scores vs. 21% (2019) and 7% (2020) for retrospective ratings. Direct observation resulted in a different level of entrustment compared with retrospective ratings in 44% of cases (p = <0.001). Conclusions: Ratings based on direct observation made unique contributions to entrustment decisions.
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 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 | 0.022 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".