Developing the SVN CLEI: A Novel Psychometric Instrument for Evaluating the Clinical Learning Environment of Student Veterinary Nurses in the UK
Bibliographic record
Abstract
Student veterinary nurses (SVNs) in the United Kingdom can spend over half their training time within the clinical learning environment (CLE) of a training veterinary practice before achieving clinical competency. Sociocultural complexities and poor management within the CLE may have a significant impact on the learning experiences of SVNs, as has been found in studies involving student human nurses. The aim of this research was to develop and validate the SVN CLE Inventory (CLEI) using principal component analysis (PCA), via a cross-sectional design, based on inventories already established in human nursing CLEs. The SVN CLEI was distributed to SVNs via an online survey over a 3-month period, generating 271 responses. PCA resulted in a valid and reliable SVN CLEI with 25 items across three factors with a total variance explained of 61.004% and an overall Cronbach's alpha (α) of .953 (factor 1: clinical supervisor support of learning [α = .935]; factor 2: pedagogical atmosphere of the practice [α = .924]; factor 3: opportunities for engagement [α = .698]). Gaining student feedback is a requirement set out by the Royal College of Veterinary Surgeons Standards Framework for Student Veterinary Nurse Education and Training, and the SVN CLEI can be used to complement the current evaluation of the training veterinary practice CLE. This will facilitate development of a more comparable, consistent, and positive experience for SVNs during clinical training in the UK.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".