Longitudinal assessment of competency development at The Ohio State University using the competency-based veterinary education (CBVE) model
Bibliographic record
Abstract
With the development of the American Association of Veterinary Medical Colleges' Competency-Based Veterinary Education (CBVE) model, veterinary schools are reorganizing curricula and assessment guidelines, especially within the clinical rotation training elements. Specifically, programs are utilizing both competencies and entrustable professional activities (EPAs) as opportunities for gathering information about student development within and across clinical rotations. However, what evidence exists that use of the central tenets of the CBVE model (competency framework, milestones and EPAs) improves our assessment practices and captures reliable and valid data to track competency development of students as they progress through their clinical year? Here, we report on validity evidence to support the use of scores from in-training evaluation report forms (ITERs) and workplace-based assessments of EPAs to evaluate competency progression within and across domains described in the CBVE, during the final year clinical training period of The Ohio State University's College of Veterinary Medicine (OSU-CVM) program. The ITER, used at the conclusion of each rotation, was modified to include the CBVE competencies that were assessed by identifying the stage of student development on a series of descriptive milestones (from pre-novice to competent). Workplace based assessments containing entrustment scales were used to assess EPAs from the CBVE model within each clinical rotation. Competency progression and entrustment scores were evaluated on each of the 31 rotations offered and high-stakes decisions regarding student performance were determined by a collective review of all the ITERs and EPAs recorded for each learner across each semester and the entire year. Results from the class of 2021, collected on approximately 190 students from 31 rotations, are reported with more than 55 299 total competency assessments combined with milestone placement and 2799 complete EPAs. Approximately 10% of the class was identified for remediation and received additional coaching support. Data collected longitudinally through the ITER on milestones provides initial validity evidence to support using the scores in higher stakes contexts such as identifying students for remediation and for determining whether students have met the necessary requirements to successfully complete the program. Data collected on entrustment scores did not, however, support such decision making. Implications are discussed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".