Spatial abilities and anatomy knowledge assessment: A systematic review
Bibliographic record
Abstract
Anatomy knowledge has been found to include both spatial and non-spatial components. However, no systematic evaluation of studies relating spatial abilities and anatomy knowledge has been undertaken. The objective of this study was to conduct a systematic review of the relationship between spatial abilities test and anatomy knowledge assessment. A literature search was done up to March 20, 2014 in Scopus and in several databases on the OvidSP and EBSCOhost platforms. Of the 556 citations obtained, 38 articles were identified and fully reviewed yielding 21 eligible articles and their quality were formally assessed. Non-significant relationships were found between spatial abilities test and anatomy knowledge assessment using essays and non-spatial multiple-choice questions. Significant relationships were observed between spatial abilities test and anatomy knowledge assessment using practical examination, three-dimensional synthesis from two-dimensional views, drawing of views, and cross-sections. Relationships between spatial abilities test and anatomy knowledge assessment using spatial multiple-choice questions were unclear. The results of this systematic review provide evidence for spatial and non-spatial methods of anatomy knowledge assessment. Anat Sci Educ 10: 235-241. © 2016 American Association of Anatomists.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".