The Influence of Spatial Ability on Medical Student Performance in the Basic Sciences
Bibliographic record
Abstract
Background Spatial visualization ability (Vz) is associated with higher performance in anatomy; however, the influence of Vz on performance in the other basic sciences has not been established. This study aimed to assess the influence of Vz on academic performance in the first year basic science curriculum at the Medical College of Georgia. We hypothesized that students’ Vz will have a significant positive correlation with their performance in structural science components (anatomy, embryology, histology, and neuroscience) and the systems‐based modules focusing on structural science. Methods First year medical students (n=83) in a systems‐based integrated curriculum completed the Mental Rotations Test (MRT) at the beginning of the year prior to the start of anatomy to establish Vz (pre‐Vz). Students completed the MRT again at the end of the year to reassess Vz (post‐Vz). Academic performance was assessed via grades in each basic science component (anatomy, biochemistry, embryology, histology, neuroscience, and physiology) and systems‐based module (Cellular and Molecular Basis of Medicine, Tissue and Musculoskeletal System, Cardiopulmonary System, Gastrointestinal and Urinary Systems, Endocrine‐Reproductive Systems, Head and Neck and Special Senses, and Medical Neuroscience). Results A two‐tailed T‐test determined that there was a significant difference between pre‐Vz and post‐Vz (12.9±4.72; 16.0±4.85 respectively, p≤0.05). ANCOVA analysis demonstrated significant positive correlations between pre‐Vz and performance in histology (r 2 =0.117), the Cardiopulmonary module (r 2 =0.114), and the Head and Neck and Special Senses module ((r 2 =0.092), p=<0.05). Significant positive correlations were found between post‐Vz and performance in anatomy (r 2 =0.106), histology (r 2 =0.130), neuroscience (r 2 =0.114), physiology (r 2 =0.095), the Tissue and Musculoskeletal System module (r 2 =0.086), the Cardiopulmonary module (r 2 =0.131), the Gastrointestinal and Urinary module (r 2 =0.094), the Head and Neck and Special Senses module (r 2 =0.158), and the Medical Neuroscience module ((r 2 =0.050), p≤0.05). Conclusions In summary, performance in one component and two modules were positively correlated with pre‐Vz scores while performance in four components and five modules were positively correlated with post‐Vz scores. These results suggest that acquired Vz, as opposed to Vz at the beginning of the year, is associated with improved performance in the basic sciences within the first year medical curriculum. This indicates that training medical students to improve their Vz may be beneficial for medical school performance. Future studies are required to assess how Vz changes throughout the first year curriculum to determine how a change in Vz correlates with student performance within the basic sciences.
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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 | 0.001 | 0.011 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".