Medical student career choice and mental rotations ability.
Bibliographic record
Abstract
PURPOSE: To determine whether innate visual-spatial ability influences medical student choice of a surgical career. In addition, the student's career interests on entering medical school (matriculation) predicted application and acceptance to a residency program. METHODS: Fifty-nine fourth year medical students at the University of Western Ontario completed a career choice questionnaire that identified the residency program(s) to which they showed interest at the time of matriculation, the program(s) to which they applied, and the residency program(s) to which they matched. The selections were compared with the student's score on the Vandenberg & Kuse Mental Rotations Test, a test of visual-spatial ability. RESULTS: Graduates initially interested in visual-spatially intense medical disciplines scored better (P < 0.02) on the Mental Rotations Test. The findings did not persist to the time of application and acceptance into residency training programs. There was no correlation between visual-spatial ability and selection of a visual-spatially intense specialty. Only 32% of graduates applied to their specialty of initial interest. CONCLUSION: The ability to rotate an object in three dimensions mentally does not to play an important role in surgical career selection although it was a predictor of initial career interest upon entry to medical school. Initial career interest was not an accurate predictor of career choice in general. In contrast to the overall results, 71% of individuals initially interested in family medicine ultimately applied to this medical discipline.
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.
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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".