Application of the One-Sample Z-test to Assess the Impact of Changes in a Veterinary Curriculum on Results From the North American Veterinary Licensing Examination (NAVLE)
Bibliographic record
Abstract
A summary report of student performance on the North American Veterinary Licensing Examination (NAVLE) is shared each year with colleges and schools of veterinary medicine accredited by the American Veterinary Medical Association Council on Education. The NAVLE summary report provides valuable outcomes assessment information that allows an institution to benchmark student clinical problem-solving ability and to monitor changes in academic performance over time. A new curriculum was fully implemented at the University of Illinois College of Veterinary Medicine for the Class of 2013. We conducted a retrospective cohort study based on annual NAVLE summary reports for 2006 to 2020 as part of a comprehensive curricular review. A One-Sample Z-test was applied to mean category-specific NAVLE scale scores or percentage correct values for each year at the University of Illinois relative to mean values for all students taking the NAVLE, with subsequent conversion of Z-score values to percentiles. P < 0.05 was considered significant. The new curriculum improved student performance on the NAVLE relative to the old curriculum, based on an increased mean yearly pass percentage ( p = .011), and increased Z-score values for 3 of 7 species categories, 7 of 12 organ system categories, and two quartiles of students categorized by class rank. Findings also identified areas of weakness in student learning, which stimulated further curricular review and revision. We suggest that colleges/schools consider using the One-Sample Z-test to increase the value of NAVLE summary data as a component of quantitative curricular review and assess the effectiveness of curricular change.
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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 | 0.037 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".