Faculty Perspectives Regarding Day One–Ready Examination Items
Bibliographic record
Abstract
The objective of this mixed-methods, cross-sectional study was to evaluate faculty perspectives regarding Day One-Ready (DOR) content on examination questions given to students at a veterinary medical college and to elucidate whether differing viewpoints on what information constitutes DOR knowledge exist among different veterinary disciplines. Twelve faculty members at a veterinary medical college from three different disciplines (small animal internal medicine, surgery, and primary care) reviewed examination questions given to veterinary students, answered the questions, and stated whether they tested DOR information. After elimination of items not answered by all respondents and after reviewing for question quality, 103 questions remained for analysis. An evaluator from each discipline participated in a discussion about DOR content. Of the questions, 30% were unanimously considered to assess DOR information. No association was found between type of question (medicine, surgery, uncategorized) and whether it was considered DOR. Primary care doctors assessed more questions as testing DOR information than either type of specialist. Questions answered correctly were more likely to be assessed as DOR. During discussion, themes identified with DOR information included common conditions, practical diagnostics, critical knowledge, and discriminating between differential diagnoses. Specialists and primary care doctors differed in their assessment of DOR questions. Veterinary faculty should carefully consider whether examination questions contain DOR information and are appropriate for testing knowledge of the entry-level veterinarian.
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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 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".