Clinical Preceptor Evaluation of Veterinary Students in a Distributed Model of Clinical Education
Bibliographic record
Abstract
The study reported here investigates the reliability and validity of a standardized evaluation form used to assess students' knowledge, clinical skills, interpersonal skills, and professionalism during fourth-year clinical rotations in a distributed model of veterinary education. A form designed to assess veterinary knowledge (5 items), clinical skills (7 items), interpersonal skills (3 items), and professionalism (6 items) was used by clinical preceptors to evaluate student performance across different rotations. For the period January--May 2007, 218 evaluations were completed for 81 students; each student was assessed in at least two rotations. Mean scores across the 21 items ranged from 3.42 (SD = 0.61) to 3.87 (SD = 0.37). Construct validity was assessed using exploratory factor analysis. The 21 items loaded on three underlying factors, professionalism, knowledge and clinical skills, accounted for 70.35% of the variance. Internal consistency (Cronbach's alpha) of each subscale was high, ranging from 0.88 for clinical skills to 0.94 for professionalism and 0.96 for the entire tool. Correlations between subscales were significant (p < 0.01), ranging from r = 0.62 to r = 0.76. Preliminary analysis suggests that the evaluation tool has good internal reliability. Construct validity analysis suggests that certain items relating to interpersonal skills and clinical skills were assessing either knowledge or professionalism. Clinical preceptors could differentiate between different skill levels for knowledge and clinical skills. Challenges associated with the assessment of professionalism are discussed.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".