Case-Based E-Learning Tool Affects Self-Confidence in Clinical Reasoning Skills Among Veterinary Students—A Survey at the Norwegian University of Life Sciences
Bibliographic record
Abstract
Veterinary education plays a crucial role in equipping veterinarians with the necessary skills and knowledge to navigate the challenges they will face in their professional careers. As part of enhancing the veterinary students’ training in clinical reasoning, an online electronic veterinary clinic was introduced to a group of students during their final semester. This platform, called Veterinary eClinic, provides access to digital, real-life clinical cases, allowing students to apply their knowledge and develop critical thinking skills in a practical context. In this research project, the veterinary students were asked to assess how confident they felt in different clinical tasks related to a clinical investigation before and after using Veterinary eClinic. An exploratory sequential mixed-methods design was used when collecting data. The students answered pre- and post-use questionnaires, and semi-structured interviews were conducted to elaborate on the quantitative results. Our results showed that the students were significantly more confident in making a problem list ( p = .005), completing diagnostic tests ( p = .022), making a diagnosis ( p = .041), and performing assessments of animal welfare in the clinic ( p = .002) after solving different clinical cases in Veterinary eClinic. As much as 97% of the respondents reported that Veterinary eClinic was a valuable learning resource in veterinary education, to a fairly large or very large extent. Our findings suggest that the use of a case-based e-learning tool might contribute to increased self-confidence in clinical reasoning skills.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".