Impact of Expert Commentary and Student Reflection on Veterinary Clinical Decision-Making Skills in an Innovative Electronic-Learning Case-Based Platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it