Introducing the VIMSSQ: Measuring susceptibility to visually induced motion sickness
Bibliographic record
Abstract
Visually induced motion sickness (VIMS) is a specific form of motion sickness caused by dynamic visual content such as Virtual Reality applications. Predicting individual susceptibility to VIMS has proven to be difficult and a reliable method has yet to emerge. Here, we introduce the Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ), a modification of the Motion Sickness Susceptibility Questionnaire uniquely designed to predict the susceptibility to VIMS specifically. Scores on the VIMSSQ are based on incidences of nausea, headache, fatigue, dizziness, and eyestrain during the past use of visual devices. In this proof-of-concept study, 71 adult participants (34 younger, 37 older) engaged in a simulated driving task and VIMS was measured using the Fast Motion Sickness Scale. Strong correlations with the reported level of VIMS were found for the nausea aspects of the VIMSSQ, suggesting that the VIMSSQ may be a useful tool to estimate individuals’ susceptibility to VIMS.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".