Comparing Entrustment and Competence: An Exploratory Look at Performance-Relevant Information in the Final Year of a Veterinary Program
Bibliographic record
Abstract
Workplace-based assessments and entrustment scales have two primary goals: providing formative information to assist students with future learning; and, determining if and when learners are ready for safe, independent practice. To date, there has not been an evaluation of the relationship between these performance-relevant information pieces in veterinary medicine. This study collected quantitative and qualitative data from a single cohort of final-year students ( n = 27) across in-training evaluation reports (ITERs) and entrustment scales in a distributed veterinary hospital environment. Here we compare progression in scoring and performance within and across student, within and across method of assessment, over time. Narrative comments were quantified using the Completed Clinical Evaluation Report Rating (CCERR) instrument to assess quality of written comments. Preliminary evidence suggests that we may be capturing different aspects of performance using these two different methods. Specifically, entrustment scale scores significantly increased over time, while ITER scores did not. Typically, comments on entrustment scale scores were more learner specific, longer, and used more of a coaching voice. Longitudinal evaluation of learner performance is important for learning and demonstration of competence; however, the method of data collection could influence how feedback is structured and how performance is ultimately judged.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".