Spatial and Visual Reasoning: Do These Abilities Improve in First-Year Veterinary Medical Students Exposed to an Integrated Curriculum?
Bibliographic record
Abstract
Spatial visualization ability refers to the human cognitive ability to form, retrieve, and manipulate mental models of spatial nature. Visual reasoning ability has been linked to spatial ability. There is currently limited information about how entry-level spatial and visual reasoning abilities may predict veterinary anatomy performance or may be enhanced with progression through the veterinary anatomy content in an integrated curriculum. The present study made use of two tests that measure spatial ability and one test that measures visual reasoning ability in veterinary students: Guay's Visualization of Views Test, adapted version (GVVT), the Mental Rotations Test (MRT), and Raven's Advanced Progressive Matrices Test, short form (RavenT). The tests were given to the entering class of veterinary students during their orientation week and at week 32 in the veterinary medical curriculum. Mean score on the MRT significantly increased from 15.2 to 20.1, and on the RavenT significantly increased from 7.5 to 8.8. When females only were evaluated, results were similar to the total class outcome; however, all three tests showed significant increases in mean scores. A positive correlation between the pre- and post-test scores was found for all three tests. The present results should be considered preliminary at best for associating anatomic learning in an integrated curriculum with spatial and visual reasoning abilities. Other components of the curriculum, for instance histology or physiology, could also influence the improved spatial visualization and visual reasoning test scores at week 32.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".