Growth Mindset in Veterinary Educators: An International Survey
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
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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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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