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Record W4380479491 · doi:10.1145/3585088.3589381

Co-Designing a Virtual Reality Intervention for Supporting Cognitive Reappraisal Skills Development with Youth

2023· article· en· W4380479491 on OpenAlexaff
Alexandra Kitson, Alissa N. Antle, Petr Slovák

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsVirtual realityExperiential learningPsychologyCognitionMental healthIntervention (counseling)Session (web analytics)Cognitive skillApplied psychologyComputer sciencePsychotherapistHuman–computer interactionPedagogy

Abstract

fetched live from OpenAlex

Cognitive reappraisal is a critical emotion regulation skill required for youth mental health. While it has been well theorized psychologically and many therapeutic approaches exist, youth still struggle to develop these skills. We explore the design space of using virtual reality to create technologically-mediated cognitive reappraisal supports for youth. We developed four VR prototypes that we used as prompts for feedback in a ten-session co-design workshop with nine youth (aged 15-17). Our work contributes youth-derived directions for further research in the design space of VR and mental health; and highlights opportunities to use VR for cognitive reappraisal skills training by generating meaningful emotionally laden situations, supporting body-based experiential learning, and providing technology-mediated social connection.

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 score0.901
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.056
GPT teacher head0.361
Teacher spread0.306 · 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

Citations27
Published2023
Admission routes1
Has abstractyes

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