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Record W4383560750 · doi:10.54254/2755-2721/5/20230517

A novel treatment program for adolescents with post traumatic stress disorder with virtual reality technology

2023· article· en· W4383560750 on OpenAlexaff
Chuanyi Zhao

Bibliographic record

VenueApplied and Computational Engineering · 2023
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsVirtual realityModalitiesTreatment modalityPopulationPosttraumatic stressPsychologyClinical psychologyVirtual Reality Exposure TherapyPsychotherapistMedicineComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

Recent advancements in the study of posttraumatic stress disorder (PTSD) have led to the discovery of innovative improvements to therapies that have already received empirical validation. The purpose of this paper is to investigate the theoretical feasibility and expected effects of a new treatment approach for the adolescent PTSD patient population that combines VR virtual reality technology with traditional treatment modalities by referring to relevant studies, literature, and survey feedback from the relevant groups. The main focus is on the use of virtual reality technology to address the reluctance of the adolescent patient population to accept treatment and to explore other possibilities for the development of a relevant target population. The limitations and drawbacks of current VR systems in the treatment of psychological disorders are also discussed, but theoretical solutions are also given. The specific role of the senses in the theoretical model and the role and usefulness for patients, physicians, and others, respectively, are also given.

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: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.031
GPT teacher head0.320
Teacher spread0.289 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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