Exploring the Mindset of Veterinary Educators for Intelligence, Clinical Reasoning, Compassion, and Morality
Bibliographic record
Abstract
Mindset theory describes whether an individual believes their intelligence can be honed and improved with effort or whether it is inherent and fixed. These two different perceptions are called growth and fixed mindsets, respectively. Previous research has shown that students with growth mindsets embrace challenges, strive for mastery, have better psychological well-being, and are more resilient than students with fixed mindsets. Mindset is contagious, and teachers’ mindsets can influence students’ mindsets, motivation, and feedback-seeking behaviors. This is the first study of the veterinary educator mindset. Previous research has shown that mindset can vary by subject or personal attributes, called domains. This study investigated mindset in four domains: intelligence, clinical reasoning, compassion, and morality. A survey was developed by combining two previously published mindset scales and was distributed electronically to the veterinary teaching faculty at St. George’s University, Grenada. The survey participants ( n = 38, response rate 56%) showed predominantly growth mindsets, with some variation by domain: for intelligence, 84.2% growth, 5.3% intermediate, 10.5% fixed mindset; for clinical reasoning, 92.1% growth, 5.3% intermediate, 2.6% fixed mindset; for compassion, 63.2% growth, 2.6% intermediate, 34.2% fixed; and for morality, 60.5% growth, 13.2% intermediate, and 26.3% fixed mindset. Fifteen participants (39.5%) had fixed mindsets in one or more domains. Twenty participants (52.6%) had growth mindsets in all four domains. There were no associations found between demographic variables and mindset. This study found that most of the veterinary teaching faculty at this university had growth mindsets in all domains investigated.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".