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Spatial Abilities, Haptic Perception, Anatomy Knowledge and Technical Skills Performance in Health Care

2020· article· en· W3020277160 on OpenAlexaffabout
J Langlois

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier Universitaire de Sherbrooke
Fundersnot available
KeywordsHaptic technologyPerceptionSpatial abilityHaptic perceptionPerspective (graphical)Object (grammar)Cognitive psychologyPsychologyCognitionComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Spatial ability has been defined as a skill in representing, transforming, generating and recalling symbolic, non‐linguistic information. Two distinct human spatial abilities have been identified: visualization and orientation. A sex difference in spatial abilities favoring male has been documented. A pattern of negative age effects on spatial abilities has also been demonstrated. Haptics has been originally defined as the science of human touch. Spatial abilities assessed by vision‐based tests were positively correlated with a drawing score based on blind haptic perception of objects and negatively correlated with time to perform correct drawings of objects from blind haptic perception. Similarly, vision‐based spatial abilities tests were correlated with pictures of objects recognized from blind haptic perception. Decreasing haptic perception while increasing working memory and cognitive load decreased performance on drawings and picture recognition of objects from blind haptic perception. Spatial abilities were correlated to the effect of working memory on drawings, but not on picture recognition of objects. These findings may be explained by the two‐dimensional (2D) nature of picture recognition and three‐dimensional (3D) nature of making a perspective drawing of an object. The interrelationship between spatial abilities tests based on vision, anatomy knowledge and technical skills performance in health care was conceptually viewed as a triangle (Figure) . Spatial abilities and anatomy knowledge An applied anatomy course was chosen by many medical graduates because of training needs rather than innate spatial abilities. In a systematic review, spatial abilities were found to be correlated to anatomy knowledge assessment using practical examination, 3D synthesis from 2D views of structure, drawing of views, and cross‐sections. Evidence was found in a systematic review for improvement of spatial abilities in anatomy education using instruction in anatomy and mental rotations training. Spatial abilities and technical skills in health care A studied sample of medical graduates over a five‐year period was not found to choose their residency programs based on their innate spatial abilities. In a systematic review, spatial abilities were found to be correlated negatively to duration and positively to the quality of technical skills performance in novices and intermediate learners. Spatial abilities test scores were found in a systematic review to be enhanced by courses in abdominal sonography and hands‐on radiology, but were not improved by residency training in General Surgery and first‐year dental curriculum. Anatomy knowledge, technical skills in health care and spatial abilities No studies were identified in a systematic review of the interrelationship between anatomy knowledge, technical skills performance and spatial abilities. Vision‐based spatial abilities tests were correlated to blind haptic perception and were found to be an important skill determinant of individual differences in learning spatial anatomy and technical skills in health care. Support or Funding Information Several research projects were supported by internal grants from the Department of Surgery, Université de Sherbrooke, Sherbrooke, QC, Canada. Figure 1

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 score0.922
Threshold uncertainty score0.323

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.237
Teacher spread0.231 · 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".

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Citations2
Published2020
Admission routes2
Has abstractyes

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