Adaptation and Validation of an Evaluation Instrument for Student Assessment of Veterinary Clinical Teaching
Bibliographic record
Abstract
There is a dearth of validated instruments for assessing clinical teaching in veterinary education. This study describes the development and validation of a veterinary-adapted Stanford Faculty Development Program 26 (SFDP-Vet22) instrument for student evaluation of veterinary clinical educators. Validity evidence was gathered in three specific categories: (a) content, (b) response process, and (c) internal structure. Content validity was supported by the educational theory and research underlying the Stanford Faculty Development Program 26 (SFDP-26) instrument. The process of adapting the SFDP-26 to the veterinary clinical education setting and piloting the SFDP-Vet22 supported validity in the response process, but straightlining indicated that some students ( n = 85) did not use the instrument as intended. Validity in internal structure was supported by the result of exploratory factor analysis with a six-factor solution. This was performed using principal axis factoring extraction and direct oblimin oblique rotation (δ = −0.3) on Box–Cox-transformed data. Twenty of the 22 items loaded in the predicted factors. Cronbach's alphas for each factor were above .846, mean inter-item correlations ranged from .594 to .794, and mean item-total correlations ranged from .693 to .854. The six-factor solution explained 75.5% of the variation, indicating a robust model. The results indicated that the control of session, communication of goals, and self-directed learning factors were stable and consistently loaded as predicted and that learning climate, evaluation, and feedback were unstable. This suggests the transference of these constructs from medical to veterinary education and supports the intended use: low-stakes decisions about clinical educator performance and identifying areas of potential growth of educators.
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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 | 0.034 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".