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Record W223035576 · doi:10.12794/metadc499989

Spatial Ability in Registered Nurses

2014· dissertation· en· W223035576 on OpenAlexaff
J. Emmett Gardner

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsCollege & Association of Registered Nurses of Alberta
Fundersnot available
KeywordsGeographyPsychologyMedicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.276
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

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