Veterinary Students' Perceptions of Their Day-One Abilities before and after Final-Year Clinical Practice-Based Training
Bibliographic record
Abstract
The present study evaluated the impact of final-year clinical practice-based training on veterinary students' perceptions of competence in "Day One" abilities by administering a pre- and post-training self-assessment checklist. This study also investigated the influence of student demographics on their perceptions of satisfaction about their own knowledge and skills and preparedness for practice. Perceptions regarding the usefulness of the checklist as a self-audit tool were also sought. Final year students (N=85) were surveyed on commencement and upon completion of the training using a checklist that had been adapted from the list of essential new-graduate abilities that was developed by the Royal College of Veterinary Surgeons and adopted by the Australasian Veterinary Boards Council. Significant improvements in student perceptions of competence were observed for 38 of the 41 abilities. Students' satisfaction with their knowledge and skill base and their perceptions of preparedness for practice were only weakly correlated with overall perceptions of competence for individual ability items and did not vary significantly with student age, gender, background, intended field and location of work, or with their work experience as veterinary nurses-if any-while studying. Two thirds of students believed that access to the self-assessment checklist on commencement of the training helped them identify areas for improvement before graduation. This article concludes that clinical practice-based training results in considerable improvement in senior veterinary students' perceptions of competence in Day One abilities and that a self-assessment checklist may help students guide their learning. Results from the present study may be useful for veterinary schools as they develop or enhance strategies used for outcomes assessment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".