Impact of Expert Commentary and Student Reflection on Veterinary Clinical Decision-Making Skills in an Innovative Electronic-Learning Case-Based Platform
Bibliographic record
Abstract
One challenge in veterinary education is bridging the divide between the nature of classroom examples (well-defined problem solving) and real world situations (ill-defined problem solving). Solving the latter often relies on experiential knowledge, which is difficult to impart to inexperienced students. A multidisciplinary team including veterinary specialists and learning scientists developed an interactive, e-learning case-based module in which students made critical decisions at five specific points (Decision Points [DPs]). After committing to each decision (Original Answers), students reflected on the thought processes of experts making similar decisions, and were allowed to revise their decisions (Revised Answers); both sets of answers were scored. In Phase I, performance of students trained using the module (E-Learning Group) and by lecture (Traditional Group) was compared on the course final examination. There was no difference in performance between the groups, suggesting that the e-learning module was as effective as traditional lecture for content delivery. In Phase II, differences between Original Answers and Revised Answers were evaluated for a larger group of students, all of whom used the module as the sole method of instruction. There was a significant improvement in scores between Original and Revised Answers for four out of five DPs (DP1, p =.004; DP2, p =.04; DP4, p <.001; DP5, p <.001). The authors conclude that the ability to rehearse clinical decision making through this tool, without direct individual feedback from an instructor, may facilitate students' transition from problem solving in a well-structured classroom setting to an ill-structured clinical setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".