MétaCan
Menu
Back to cohort
Record W4387905770 · doi:10.18332/ejm/172187

Immersive Virtual Reality (VR) when learning anatomy in midwifery education

2023· article· en· W4387905770 on OpenAlexfundno aff
Katrine Aasekjær, Bente Bjørnås, Halldis Skibenes, Eline Skirnisdottir Vik

Bibliographic record

VenueEuropean Journal of Midwifery · 2023
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsnot available
FundersCentrum fÖr Personcentrerad VårdStockholms Läns LandstingSocial Sciences and Humanities Research Council of CanadaInstitut National d'assurance Maladie-InvaliditéVlaamse regeringPetroleum Technology Development FundHellenic Foundation for Research and InnovationHaute école Spécialisée de Suisse OccidentaleVedecká Grantová Agentúra MŠVVaŠ SR a SAVGöteborgs UniversitetTürkiye Bilimsel ve Teknolojik Araştırma KurumuCity, University of LondonVetenskapsrådetLaerdal Foundation for Acute MedicineSuomalainen Lääkäriseura DuodecimAmasya ÜniversitesiKnut och Alice Wallenbergs StiftelseKarolinska InstitutetOpetushallitusEuropean CommissionUK Research and InnovationInnovationsfondenBundesministerium für Bildung und ForschungFinska LäkaresällskapetRoyal College of Midwives
KeywordsVirtual realityVirtual microscopyAnatomyComputer scienceMedicineMultimediaHuman–computer interactionPathology

Abstract

fetched live from OpenAlex

is an open access and double-blind peer-reviewed scientific journal, that encompasses all aspects of the practice of midwifery, especially focused on midwifery research, support, care and advice during pregnancy, labour and the postpartum period.The overall aim of the journal is to foster, promote and disseminate research involving midwifery education and clinical practice.While the journal is European, it warmly welcomes and publishes submissions and content from all over the world, linking the global community.

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)0.014
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.066
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.001

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 head0.012
GPT teacher head0.245
Teacher spread0.232 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of MidwiferySame topicAnatomy and Medical TechnologyFrench-language works237,207