Benefits and Challenges of Developing a Customized Rubric for Curricular Review of a Residency Program in Laboratory Animal Medicine
Bibliographic record
Abstract
Rigorous curricular review of post-graduate veterinary medical residency programs is in the best interest of program directors in light of the requirements and needs of specialty colleges, graduate school administrations, and other stakeholders including prospective students and employers. Although minimum standards for training are typically provided by specialty colleges, mechanisms for evaluation are left to the discretion of program directors. The paucity of information available describing best practices for curricular assessment of veterinary medical specialty training programs makes resources from other medical fields essential to informing the assessment process. Here we describe the development of a rubric used to evaluate courses in a 3-year American College of Laboratory Animal Medicine (ACLAM)-recognized residency training program culminating in a Master of Science degree. This rubric, based on examples from medical education and other fields of graduate study, provided transparent criteria for evaluation that were consistent with stakeholder needs and institutional initiatives. However, its use caused delays in the curricular review process as two significant obstacles to refinement were brought to light: variation in formal education in curriculum design and significant differences in teaching philosophies among faculty. The evaluation process was able to move forward after institutional resources were used to provide faculty development in curriculum design. The use of a customized rubric is recommended as a best practice for curricular refinement for residency programs because it results in transparency of the review process and can reveal obstacles to change that would otherwise remain unaddressed.
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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 | 0.284 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".