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Record W4205701438 · doi:10.5539/cis.v15n1p47

VR Users Are More Relaxed and Optimistic during COVID Lockdown than Others

2022· article· en· W4205701438 on OpenAlexvenueno aff
Erik Geslin, Erik Hammer

Bibliographic record

VenueComputer and Information Science · 2022
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsHeadsetVirtual realityFeelingCoronavirus disease 2019 (COVID-19)Immersion (mathematics)Computer scienceHuman–computer interactionOptical head-mounted displayPeriod (music)Positive correlationApplied psychologyInternet privacyPsychologyArtificial intelligenceMedicineSocial psychologyAestheticsTelecommunications

Abstract

fetched live from OpenAlex

Numerous studies have shown how the confinement period linked to the health situation of COVID-19 (SARS-CoV2) has been psychologically destructive for many people. These psychological disorders are generally linked to confinement and isolation. At the same time, virtual reality tools are becoming more popular, the level of immersion in cheap HMD (Head Mounted Display) helmets today allows access to a satisfactory level of presence. Our theory, widely supported by substantial literature, is that the regular use of immersive Virtual Reality during periods of confinement could allow users to better endure the psychological constraints. We observed the correlation between the weekly use of HMD Virtual Reality headsets, during the COVID-19 confinement period and the levels of well-being of users. Our study involved n = 56 participants divided into two groups of users and non-users of HMD headset. They answered 2 SWEMWBS and ONS questionnaires plus a social trust question. The results show a significant correlation between the use of virtual reality headset during COVID-19 lockdown and the level of relaxation and optimistic feeling but also a correlation between this use of VR (Virtual Reality) HMD and the feeling of being less close to others during the same period.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.603
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.005
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2022
Admission routes1
Has abstractyes

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