The study of visuospatial abilities in trainees: A scoping review and proposed model
Bibliographic record
Abstract
BACKGROUND: Visuospatial abilities are an important component of technical skill acquisition. Targeted visuospatial ability training may have positive implications for training programs. The development of such interventions requires an adequate understanding of the visuospatial ability processes necessary for surgical and nonsurgical tasks. This scoping review aims to identify the components of visuospatial ability that have been reported in surgical and nonsurgical trainees and determine if there is consensus regarding the language and psychometric measures used, clarifying the elements that may be required to develop interventions that enhance visuospatial ability. METHODS: A scoping review was designed to identify relevant records from EMBASE and Medline until January 13, 2020. Data were extracted on visuospatial ability terminology, dimensions, instruments, and interventions with results stratified by specialty (surgical, nonsurgical, or mixed). Conference abstracts, opinion pieces, and review studies were excluded. RESULTS: Out of 882 total records, 26 were identified that met criteria for inclusion. Surgical specialities were represented in > 90% of results. A total of 16 unique terms were used to describe visuospatial ability and were measured using 34 instruments, of which eight were used more than once. Eighteen different dimensions were identified. A single study explored the effects of a targeted visuospatial ability intervention. CONCLUSION: A wide range of visuospatial ability terms, instruments, and dimensions were identified, suggesting an incomplete understanding of the components most relevant to surgical and nonsurgical tasks. This confusion may be hindering the development of visuospatial ability targeted interventions during residency training. A rigorous methodological model is proposed to help unify the field and guide future research.
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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 | 0.053 | 0.095 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.041 | 0.042 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".