Growth Mindset in Veterinary Educators: An International Survey
Bibliographic record
Abstract
Carol Dweck's mindset theory describes whether an individual believes that attributes, like intelligence or morality, can be honed (growth mindset) or are innate (fixed mindset). An educator's mindset impacts their approach to teaching, students' learning, participation in faculty development, and well-being. Mindset can affect faculty members' openness to curricular change, making the study of veterinary educator mindset timely and salient, as competency-based education is spurring curricular change worldwide. The purpose of this study was to examine the mindsets of veterinary educators internationally. A survey, consisting of demographic questions and mindset items (based on previously published scales), was distributed electronically to veterinary educators internationally, at universities where English is the primary instruction medium. Mindset was evaluated for the following traits: intelligence, clinical reasoning, compassion, and morality. Scale validation, descriptive statistics, and associations to demographic variables were evaluated. A total of 446 complete surveys were received. Overall, the study population demonstrated predominantly growth mindsets for all traits, higher than population averages, with some variation by trait. There was a small effect on years teaching toward growth mindset. No other associations were found. Veterinary educators internationally who participated in this study demonstrated higher rates of growth mindset than the general population. In other fields, a growth mindset in educators has had implications for faculty well-being, teaching and assessment practices, participation in faculty development, and openness to curricular change. Further research is needed in veterinary education to evaluate the implications of these high rates of growth mindset.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".