Understanding Veterinary Practitioners' Decision-Making Process: Implications for Veterinary Medical Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding how veterinary practitioners make clinical decisions, and how they use scientific information to inform their decisions, is important to optimize animal care, client satisfaction, and veterinary education. We aimed to develop an understanding of private practitioners' process of decision making. On the basis of a grounded-theory qualitative approach, we conducted a telephone survey and semi-structured face-to-face interviews. We identified a decision-making framework consisting of two possible processes to make decisions, five steps in the management of a clinical case, and three influencing factors. To inform their decision, veterinary surgeons rarely take the evidence-based medicine (EBM) approach. They consult first-opinion colleagues, specialists, laboratories, and the Internet rather than scientific databases and peer-reviewed literature, mainly because of limited time. Most interviewees suggested the development of educational interventions to better develop decision-making skills in veterinary schools. Adequate information and EBM tools are needed to optimize the time spent in query and assessment of scientific information, and practitioners need to be trained in their use.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 it