Evaluation of the Impact of Using a Simulator for Teaching Veterinary Students Cerebrospinal Fluid Collection: A Mixed-Methods Study
Bibliographic record
Abstract
The collection of cerebrospinal fluid (CSF) plays a pivotal role in the diagnosis of central nervous system diseases. Prior training in this invasive procedure is essential to minimize the risk of harming animals. Because of this risk, stress and anxiety can influence the learning process. Simulators can be used to teach and learn invasive procedures. The aim of this mixed-methods study was to validate a CSF collection simulator and investigate students’ perceptions of the simulator as an educational tool. The quantitative approach validated the simulator for face and content validity, and students provided a general evaluation of the simulator using surveys. The simulator’s construct validity was measured by means of a global rating scale. Students’ perceptions were investigated qualitatively using semi-structured interviews. Experts ( n = 13) confirmed the simulator’s face and content validity. Students ( n = 16) evaluated the simulator as supportive of their learning. Results for construct validity demonstrated higher global rating scores from experts than from students. The scores for procedural performance and procedural knowledge and flow showed significant differences ( p ≤ .05). Analysis of interviews with students ( n = 10) revealed four main themes: emotions, learning process, evaluation of the model, and CSF collection procedure. In conclusion, this study validated the use of the CSF simulator as an educational tool that can help students overcome some of their anxiety in relation to performing an invasive procedure.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".