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Record W4417076250 · doi:10.1163/22134808-bja10180

German Translation and Validation of the Visually Induced Motion Sickness Susceptibility Questionnaire Short (VIMSSQ-short)

2025· article· en· W4417076250 on OpenAlexaff
Mara Baljan, John F. Golding, Heiko Hecht, Behrang Keshavarz

Bibliographic record

VenueMultisensory Research · 2025
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsToronto Rehabilitation InstituteToronto Metropolitan UniversityUniversity Health Network
Fundersnot available
KeywordsMotion sicknessGermanMotion (physics)NormativeRelevance (law)Simulator sickness

Abstract

fetched live from OpenAlex

Motion sickness is a condition that is characterized by symptoms like dizziness, nausea, or vomiting, especially during transportation or immersive visual experiences such as gaming and virtual reality (VR). Visually Induced Motion Sickness (VIMS) is of particular concern due to its increasing relevance with the rise of immersive technologies. The 6-item version of the Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ-short), a modified version of the established Motion Sickness Susceptibility Questionnaire, was developed to quickly assess individual susceptibility to VIMS. This study focuses on its translation into German and the validation of this German-language version of the VIMSSQ-short. The translation process included independent translations by experts and a back-translation to identify and resolve discrepancies. An online survey collected normative data from 200 participants, revealing a mean score of 5.85 (SD = 3.31) for the translated VIMSSQ-short. The results indicated significant gender differences, with females exhibiting higher susceptibility scores than males. Additionally, a significant negative correlation between age and susceptibility was observed. An experimental study involving 70 participants further confirmed these findings in terms of mean scores, gender, and age. Additionally, the findings demonstrate that higher VIMSSQ scores predict symptom severity during VR exposure ( r s = 0.58 with Simulator Sickness Questionnaire total score). Overall, the translated VIMSSQ-short shows promise as a reliable tool for assessing VIMS susceptibility in German-speaking populations, contributing to the understanding of motion sickness in immersive environments. The identification of susceptible individuals is relevant both for practical applications (e.g. in the training of emergency forces) and in experimental settings for the randomization or screening of participants.

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)0.005
metaresearch head score (Gemma)0.008
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: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.155
GPT teacher head0.437
Teacher spread0.281 · 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
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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