Evaluation of the Level of Difficulty of Patient Cases for Veterinary Problem-Solving Examination: A Preliminary Comparison of Three Taxonomies of Learning
Bibliographic record
Abstract
An important issue that has received insufficient attention in the use of problem-based learning in the medical curriculum is the mode of assessing the level of difficulty of patient cases. In the present study, the level of difficulty of case-based questions in a veterinary degree final examination in reproduction was evaluated. First, cognitive taxonomies were evaluated to clarify whether qualitative methods such as Bloom's taxonomy, the Structure of the Observed Learning Outcome (SOLO) taxonomy, and the Amsterdam Clinical Challenge Scale (ACCS) differed from each other as evaluation tools for problem-based cases. Using these taxonomies, 30 case-based questions from the final examination in reproduction in the Helsinki veterinary program were initially evaluated to determine which one was best suited to the evaluation of the difficulty of cases. In follow-up, the same cases were also evaluated by an experienced veterinary instructor in reproduction, with the aim of gaining insight into using these approaches to evaluating difficulty. It would appear, from this preliminary assessment, that the SOLO taxonomy may be the most suitable for evaluating the difficulty of patient cases, since the instructor's quality rating resembled more closely the SOLO than the Bloom taxonomy or the ACCS. It is to be emphasized that the purpose of this study was to provide a preliminary evaluation of possible approaches that might be used to assess patient-case difficulty. Resolving all issues will require a greater number of evaluations of all components.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".