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Record W1964406984 · doi:10.1145/2702123.2702344

The Virtual Meditative Walk

2015· article· en· W1964406984 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDistractionMeditationVirtual realityBiofeedbackChronic painMindfulnessMindfulness meditationComputer sciencePsychologyHuman–computer interactionPhysical medicine and rehabilitationMedicinePhysical therapyPsychotherapistCognitive psychology

Abstract

fetched live from OpenAlex

Because the nature of chronic pain is complex, pharmacological analgesics are often not enough to achieve an ideal treatment plan. Virtual Reality (VR) technologies have emerged within medical research in recent years for treating acute pain, and proved to be an effective strategy based on pain distraction. This paper describes a VR system designed for chronic pain patients. The system incorporates biofeedback sensors, an immersive virtual environment, and stereoscopic sound titled the "Virtual Meditative Walk" (VMW). It was designed to enable chronic pain patients to learn Mindfulness-based stress reduction (MBSR), a form of meditation. By providing real-time visual and sonic feedback, VMW enables patients to learn how to manage their pain. A proof-of-concept user study was conducted to investigate the effectiveness of the VR system with chronic pain patients in clinical settings. Results show that the VMW was more effective in reducing perceived pain compared to the non-VR control condition.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.998

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)0.0170.003

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 head0.074
GPT teacher head0.379
Teacher spread0.305 · 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

Quick stats

Citations188
Published2015
Admission routes1
Has abstractyes

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