Veterinary Nursing Students’ Experience in the Clinical Learning Environment and Factors Affecting Their Perception
Bibliographic record
Abstract
Student veterinary nurses (SVNs) spend a significant proportion of their training time within the clinical learning environment (CLE) of a veterinary practice. These clinical experiences are vital for building practical and professional skills. To evaluate the current satisfaction of SVNs in the CLE, a cross-sectional survey design was used incorporating a previously validated instrument. To provide understanding of factors that may affect the SVN satisfaction, additional validated tools were added across factors, including resilience, well-being, personality, and workplace belonging. A total of 171 SVNs completed the survey. In addition, two open questions were included to provide greater depth of understanding of the SVN experiences. Results showed that 70.76% of respondents were satisfied/very satisfied with the CLE. Significant factors that affected the satisfaction scores included, depression, anxiety, and stress ( p ≤ .001), psychological sense of organizational membership ( p ≤ .001), agreeableness ( p = .022), and emotional stability ( p = .012). The qualitative data demonstrated shared SVN factors that are considered to contribute to clinical learning and those that detract from clinical learning. Educational facilities and training veterinary practices can support the SVN within the CLE by creating a greater sense of belonging, considering the SVN individual personality and well-being, and including the SVN in discussions around learning support needs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".