Investigating Visual–Spatial Abilities in Students and Expert Physical Therapists
Bibliographic record
Abstract
Purpose: Visual–spatial abilities (VSAs) – the aptitude for mentally processing, retaining, and manipulating visual input – are used by physical therapists in movement analysis. Superior VSAs have been demonstrated in experts compared with novices in other fields, including surgery, anatomy, and aviation, but no literature has investigated VSAs in physical therapy. The purpose of this study was to quantify VSAs in expert and student physical therapists and investigate the differences between the two groups. Our results could assist future researchers in identifying areas for skill development and improved clinical competency in students and novice therapists. Method: Expert physical therapists and first-year PT students completed four computerized VSA tests in the Psychology Experiment Building Language programme: Four-Choice Response, Sequential Pattern Comparison, Mental Rotation, and Situation Awareness. Results: A total of 16 participants were recruited for each group. Expert physical therapists responded more accurately to the Four-Choice Response test, but not significantly so ( p = 0.06), and with a significantly slower response time than student physical therapists ( p = 0.03). No other differences were found. Conclusions: These findings suggest that expert physical therapists use selective attention more effectively and may value accuracy over speed. No differences were found in other measures of VSAs. Further studies are required to confirm and expand our findings.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".