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Record W4390989028 · doi:10.5267/j.ijdns.2023.11.011

Virtual reality socialization groups on Facebook: A new frontier for children with social anxiety disorder

2024· article· en· W4390989028 on OpenAlexvenueno aff
Hussein Alsrehan, Rakan Alhrahsheh, Shirin S. AlOdwan, Khaled Khamis Nser, Saddam Rateb Darawsheh, Mohamad Ahmad Saleem Khasawneh, Mona Zayed Sayed Owis

Bibliographic record

VenueInternational Journal of Data and Network Science · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersKing Khalid UniversityUtah Agricultural Experiment Station
KeywordsSadnessSocializationPsychologyPsychological interventionSocial anxietyAnxietyIntervention (counseling)Virtual realityVariety (cybernetics)FrontierSocial mediaDevelopmental psychologyClinical psychologySocial psychologyPolitical scienceAngerComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

As a potential treatment intervention for youngsters with Social Anxiety Disorder (SAD) in UAE, this study aims to investigate the use of virtual reality socialization groups on the widely used social media platform Facebook. The purpose of this research is to examine how social anxiety, sadness, and anxiety symptoms change after receiving treatment. The quantitative approach used in the study yields very small impact sizes. No discernible differences emerged, however, between the test and control groups at the statistical level. It investigates how cultural factors, including the unique social norms and expectations in UAE, play a crucial role in determining the success of interventions. Specifically adapted virtual reality therapies for various cultural settings are emphasized. In addition, it highlights the importance of understanding the therapeutic implications of small effect sizes. Prospective research directions are explored in this article; special attention is paid to the development of ever-better technologically-driven treatments across a variety of cultural settings.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.721
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.053
GPT teacher head0.415
Teacher spread0.362 · 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 designOther design
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

Citations8
Published2024
Admission routes1
Has abstractyes

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