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Record W4206185174 · doi:10.2196/29164

Immersive Reality Experience Technology for Reducing Social Isolation and Improving Social Connectedness and Well-being of Children and Young People Who Are Hospitalized: Open Trial

2021· article· en· W4206185174 on OpenAlexvenueno aff
Hiran Thabrew, Laura Ann Chubb, Harshali Kumar, Christa Fouché

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2021
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSocial connectednessVirtual realitySocial isolationPsychologyScale (ratio)Isolation (microbiology)MedicineSocial psychologyPsychotherapistComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Children and young people who are hospitalized can feel disconnected from their peers and families, which can, in turn, predispose them to psychological problems, including anxiety and depression. Immersive reality experience technology, recently developed by the New Zealand Patience Project Charitable Trust, may help to overcome these issues. Immersive reality experience technology uses immersive 360° live streaming and a virtual reality headset to enable children and young people who are hospitalized to connect through cameras located in either their school or home environment and via SMS text messaging with a designated buddy. OBJECTIVE: This trial aims to expand qualitative findings from a previous smaller proof of concept trial to ascertain the views of New Zealand children and young people who are hospitalized, their caregivers, and teachers regarding immersive reality experience technology and quantitatively evaluate the effectiveness of immersive reality experience technology in reducing social isolation and improving social connectedness and well-being using validated outcome measures. METHODS: An open trial of immersive reality experience technology was conducted between December 2019 and December 2020 for which 19 New Zealand children and young people aged 13 to 18 years who had been hospitalized at Starship Hospital-a specialist pediatric hospital in Auckland-for at least 2 weeks were recruited. All young people completed the Short Warwick-Edinburgh Mental Well-Being Scale, an abbreviated version of the Social Connectedness Scale, and the Social Inclusion Scale at baseline. Of the 19 participants, 10 (53%) used immersive reality experience technology as often as they wished over a 6-week period and completed postintervention measures. Semistructured interviews with a subset of the 10 young people, 4 caregivers, and 6 teachers were conducted immediately after the intervention. RESULTS: Participants reported improvements in social inclusion (mean change 3.9, SD 2.8; P=.06), social connectedness (mean change 14.2, SD 10.0; P=.002), and well-being (mean change 5.7, SD 4.0; P=.001). Key themes from interviews with participants, caregivers, and teachers were the importance of support for using immersive reality experience technology, connecting versus connectedness, choice and connection, and the value of setting it up and getting it right. Recommendations for improving connectedness via immersive reality experience and related technologies were also provided. CONCLUSIONS: Immersive reality experience technology can improve the social inclusion, social connectedness, and well-being of New Zealand children and young people who are hospitalized. With some technological modifications and simplified implementation, immersive reality experience and related technology could become part of standard care and support children and young people who are hospitalized in New Zealand and elsewhere to sustain family and peer cohesion, experience fewer psychological problems, and more easily return to normal life following the completion of treatment. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Network Registry ACTRN12619000252112; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=376837&isReview=true.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.311
Teacher spread0.296 · 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 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

Citations17
Published2021
Admission routes1
Has abstractyes

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