Bibliographic record
Abstract
Spatial ability is the skill associated with mental relations among objects, the process of maintaining the physical aspects of an object after mentally rotating it in space. Many studies report a strong association of spatial ability with success in various areas of health care, especially surgery, radiology and dentistry. To date, similar investigations in professional nursing could not be located. Registered nurses, employed in an acute care multi-hospital setting, were surveyed using the Shipley-2Block Pattern Test, the Group Embedded Figures Test, and a newly created test of general nursing knowledge. The sample size of 123 nurses was composed of 31 male nurses and 92 female nurses. Data was collected between May and August of 2013 and analyzed using R, version 2.15.2. The present study did not demonstrate a statistically significant effect for gender differences on two measures of spatial ability. However, Cohen’s d effect sizes for mean gender differences in the present study are consistent with prior studies. This may suggest the nursing profession is comparable with other professions where males perform higher than females on spatial ability. The present study should be considered an initial step toward evaluating the relevance of spatial ability in the performance of nursing care.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".