Diagnostic Imaging Learning Resources Evaluated by Students and Recent Graduates
Bibliographic record
Abstract
Many learning resources can help students develop the problem-solving abilities and clinical skills required for diagnostic imaging. This study explored veterinary students' perceptions of the usefulness of a variety of learning resources. Perceived resource usefulness was measured for different levels of students and for academic versus clinical preparation. Third-year (n=139) and final (fifth) year (n=105) students and recent graduates (n=56) completed questionnaires on perceived usefulness of each resource. Resources were grouped for comparison: abstract/low complexity (e.g., notes, multimedia presentations), abstract/high complexity (e.g., Web-based and film case repositories), concrete/low complexity (e.g., large-group "clicker" workshops), and concrete/high complexity (e.g., small-group interpretation workshops). Lower-level students considered abstract/low-complexity resources more useful for academic preparation and concrete resources more useful for clinical preparation. Higher-level students/recent graduates also considered abstract/low-complexity resources more useful for academic preparation. For all levels, lecture notes were considered highly useful. Multimedia slideshows were an interactive complement to notes. The usefulness of a Web-based case repository was limited by accessibility problems and difficulty. Traditional abstract/low-complexity resources were considered useful for more levels and contexts than expected. Concrete/high-complexity resources need to better represent clinical practice to be considered more useful for clinical preparation.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".