Norms and Correlations of the Visually Induced Motion Sickness Susceptibility Questionnaire Short (VIMSSQ-short)
Bibliographic record
Abstract
The short version of the Visually Induced Motion Sickness Susceptibility Questionnaires (VIMSSQ-short) was designed to estimate an individual's susceptibility to motion sickness caused by exposure to visual motion, for instance when using smartphones, simulators, or Virtual Reality. The goal of the present paper was to establish normative data of the VIMSSQ-short for men and women based on online surveys and to compare these results with findings from previously published work. VIMSSQ-short data from 920 participants were collected across four online surveys. In addition, the relationship with other relevant constructs such as susceptibilities to classic motion sickness (via the Motion Sickness Susceptibility Questionnaires (MSSQ)), Migraine, Dizziness, and Syncope, was explored. Normative data for the VIMSSQ-short showed a mean score of M = 7.2 (standard deviation (SD) = 4.2) and a median of 7, with a good test reliability (Cronbach's alpha = 0.80). No significant difference between men and women showed. The VIMSSQ-short correlated significantly with the MSSQ ( r = 0.55), Migraine ( r = 0.48), Dizziness ( r = 0.35), and Syncope ( r = 0.31). Exploratory factor analysis of all variables suggested two latent variables: nausea-related and oculomotor-related. Norms for this study were consistent with the only other large online survey. But average VIMSSQ-short values were lower in smaller studies of participants volunteering for cybersickness experiments, perhaps reflecting self-selection bias. The VIMSSQ-short provides reliability with efficient compromise between length and validity. It can be used alone or with other questionnaires, the most useful being the MSSQ and the Migraine Screening Questionnaire.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".