Implementation of a Clinical Entrustment Scale and Feedback Form in an Academic Veterinary Medical Center: An Empirical Analysis of Goal Oriented Learner Driven-Entrustment (GOLD-E) Assessment Tool
Bibliographic record
Abstract
This paper presents findings from an empirical analysis conducted on the initial implementation of Goal Oriented Learner Driven-Entrustment (GOLD-E). Specifically, researchers examined the following questions: How do faculty, technicians, and residents/interns integrate GOLD-E into their assessment process? Is GOLD-E user friendly (e.g., form and functionality)? How do faculty, technicians, and residents/interns navigate the shift from evaluator to coach? Researchers incorporated a number of mixed, overlapping methodologies consisting of both qualitative and quantitative survey responses and focus group interactions. The use of these multiple data representations allowed researchers to gather layered and complex data to provide for a fuller understanding of the initial implementation of the GOLD-E tool. The empirical analysis demonstrates the need for revisions in the GOLD-E assessment tool as well as broad systemic changes to drive transformation in the culture of 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 | 0.121 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".