Arizona Veterinarians’ Perceptions and Consensus Regarding Skills, Knowledge, and Attributes of Day One Veterinary Graduates
Bibliographic record
Abstract
The purpose of this study was to assess Arizona veterinarians’ perceptions and consensus regarding the importance of items in the domains of clinical skills, knowledge, and attributes of Day One graduates of veterinary school and to determine demographic predictors for items on which consensus was low. In this survey-based prospective study, respondents were asked to rate the importance of 44 items on a 5-point Likert scale ranging from 1 ( not at all important) to 5 ( extremely important). Responses were visualized as divergent stacked bar charts and evaluated via summary quantitative and qualitative analyses. Several items had a median score of 5. For clinical skills, items were the ability to formulate a preventive health care plan, the ability to interpret test results, and basic safe handling and restraint of animals; for knowledge, knowledge of pain management and anesthesia; and for attributes, teamwork, problem-solving skills, and client communication skills. The majority of items (80%) had a strong or very strong consensus measure, 18% had a moderate consensus measure, and 2% had a weak consensus measure. Six items (14%) varied by at least one demographic category. We found demographic differences between large and small animal practices in the clinical skill of ability to perform a necropsy, knowledge of large animal theriogenology, and knowledge of canine theriogenology. In conclusion, we found differences in the importance of items and agreement among practitioners, suggesting that critical evaluation of the mapped curriculum, particularly with regard to core curriculum compared with electives and clinical tracks, may benefit students and future employers.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".