Evaluating the Quality of Veterinary Students' Experiences of Learning in Clinics
Bibliographic record
Abstract
Educators seeking to evaluate the quality of students' experiences of clinic-based learning (CBL) face a challenging task. CBL programs provide multiple opportunities for learning and aim to develop a wide range of skills, knowledge, and capacities. While direct observation of learners provides important information about students' proficiency in performing various clinical tasks, more comprehensive measures are required to unpack and identify factors relating to practice readiness as a whole. This study identified variables that have a logical and statistically significant association with learning outcomes across the broad range of attributes expected of new graduate veterinarians. The research revealed that the extent of final-year veterinary students' practice readiness, as assessed by placement supervisors against criteria relevant to new graduate practice, is related to the quality of their conceptions of and approaches to CBL. Students' conceptions of and approaches to CBL were evaluated using quantitative survey instruments, with a 93% response rate (N=100) obtained for the two questionnaires. Descriptive and exploratory statistics were used to link qualitative differences in students' conceptions of and approaches to CBL with performance against criteria relevant to new graduate practice. Students who reported poorer-quality conceptions of and approaches to CBL (n=38) attained lower levels of achievement than students who reported better-quality conceptions of and approaches to CBL (n=55). Evaluation of students' conceptions of and approaches to CBL can be used by educators seeking to evaluate and improve the extent to which CBL programs are achieving their desired goals.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".