A Model for Residency Training: The Small Animal Emergency and Critical Care Residency Program at Purdue University
Bibliographic record
Abstract
Training residents in any specialty is a balancing act between ensuring high-quality education, making certain the resident meets the requirements set forth by the specialty college to achieve credentials and be eligible to take the board certification examination, and fulfilling clinical duties at that institution. For programs such as this one, residents are integral members of the clinical team, working primary emergency receiving shifts in order to allow the service to function; this leads to a need to identify and protect learning time for the residents. Those involved in the Purdue Small Animal Emergency and Critical Care residency program believe that in the chaos that is emergency and critical care, a firm timeline with attainable checkpoints is crucial to resident success. Such a timeline follows goal-setting theory and provides structure and guidance to candidates to navigate their 3-year program and ensure that they complete all requirements during the residency period. Candidates completing the Purdue program successfully finish their credentials, including at least one first author publication, and have at least one scientific presentation to improve their curriculum vitae. This article serves to present the structure and timelines used by the Purdue Small Animal Emergency and Critical Care program to organize its residency program as an example of a successful program.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".