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Effect of Interventions on Spatial Abilities in Anatomy Education: A Systematic Review and Meta‐Analysis

2018· review· en· W3173817199 on OpenAlexaff
J Langlois, Christian Bellemare, Josée Toulouse, George A. Wells

Bibliographic record

VenueThe FASEB Journal · 2018
Typereview
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of OttawaCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité de SherbrookeCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier Universitaire de Sherbrooke
Fundersnot available
KeywordsCINAHLPsycINFOPsychological interventionScopusTest (biology)MEDLINEIntervention (counseling)Spatial abilityPsychologyStandardized testMedicineMedical educationMathematics educationNursing

Abstract

fetched live from OpenAlex

Objective Spatial abilities have been correlated to anatomy knowledge assessment using practical examination, 3D‐synthesis from 2D‐views, drawing of views, and cross‐sections in a previous systematic review. The objective of this study was to evaluate the effect of interventions on spatial abilities in the field of anatomy education. Methods Search criteria included ‘interventions', ‘spatial abilities' and ‘anatomy’. Keywords related to these criteria were defined. A literature search was conducted to August 3, 2017 in Scopus and EBSCOhost platform (Medline with Full Text, Cinahl Plus with Full Text, ERIC, Education Source, and PsycInfo). Citations were reviewed and those involving the field of anatomy education, an intervention, and a spatial abilities test were retained and the corresponding full‐text articles were reviewed for inclusion. Citations related to abstracts, literature reviews, books, book sections, and theses were excluded. Citations and full‐text articles were reviewed by two independent investigators. Methods in the field of anatomy education relating an intervention to spatial abilities test scores obtained before and after the intervention were identified as eligible. Eligible articles were reviewed in a systematic way and assessed for quality using Scottish Intercollegiate Guidelines Network‐50 assessment instruments. Effect sizes using standardized mean differences were calculated since different measurement scales for spatial abilities test scores were used. If I 2 was greater than 50%, then heterogeneity was formally evaluated and random effects model was considered. Results Of the 2405 citations obtained, 52 articles were identified and reviewed, yielding 8 eligible articles. Instruction in anatomy and mental rotations training were found to improve spatial abilities. For the 7 studies retained for the meta‐analysis that included the effect of interventions on spatial abilities test scores, the pooled difference was 0.68 [95% CI (0.40; 0.95); n = 11] improvement with an I 2 of 82%. For the 2 studies that included the practice effect on spatial abilities test scores in a control group, the pooled difference was 0.59 [95% CI (0.41; 0.76); n = 2] improvement with an I 2 of 0%. In these 2 studies, the impact of the intervention on spatial abilities test scores was found valid despite the practice effect. Conclusion Evidence was found for improvement of spatial abilities in the field of anatomy education using instruction in anatomy and mental rotations training. Support or Funding Information Funding: none. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.039
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0180.025
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.030
GPT teacher head0.355
Teacher spread0.325 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations0
Published2018
Admission routes1
Has abstractyes

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