Effects of a Curricular Revision on Learner Outcomes in Veterinary Clinical Pathology
Bibliographic record
Abstract
A mixed-methods evaluation was conducted to study learner attitudes and knowledge about clinical pathology across a curricular change that instituted a stand-alone clinical pathology course in place of content within a previously integrated pathology course structure. Groups of pre- and post-change students were assessed three times across the two semesters leading up to graduation. At each time, rank-ordered and open-ended response items probed attitudes, and multiple-choice items assessed knowledge. Data about student clinical pathology performance were also collected from clinical pathology instructors and supervising clinicians. Student rank-ordered items were evaluated by factor analysis; resulting factor-scale scores, multiple-choice scores, and rank responses from study cohorts were statistically assessed between groups and within each group over time. Intraclass correlations were calculated for the coding of student open-ended responses, and all coded responses were compared among groups. Analysis revealed that students in the revised curriculum had greater satisfaction with their training and greater confidence in data interpretation compared to students without exposure to an independent clinical pathology course. Although differences in knowledge of clinical pathology were not detected, it was also apparent that the independent clinical pathology course filled a student-perceived curricular need without raising criticisms related to diminished integration with anatomic pathology. Secondary study outcomes included formative feedback for course improvement, evidence of clerkship efficacy, and baseline data for further studies.
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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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".