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Record W4224318247 · doi:10.4103/ijoy.ijoy_104_21

East Meets West in Therapeutic Approaches to the Management of Chronic Pain

2022· article· en· W4224318247 on OpenAlexaff
Eleni G. Hapidou, Ting Qi Amy Huang

Bibliographic record

VenueInternational Journal of Yoga · 2022
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsMcMaster UniversityHamilton Health SciencesMcMaster University Medical Centre
Fundersnot available
KeywordsBiopsychosocial modelMeditationMindfulnessPsychotherapistChronic painPerspective (graphical)Relaxation (psychology)Mindfulness meditationPsychologyCognitionRelaxation TherapyPain managementAlternative medicineClinical psychologyMedicinePhysical therapyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Yoga as a holistic principle, not only practice of asanas or poses, integrates all aspects of the self, with biological, mental, intellectual, and spiritual elements. Yoga encompasses the biopsychosocial medical perspective, which regards pain as a dynamic interaction between physiological, psychological, and social factors. The purpose of this perspective article is to compare and contrast psychological practices such as mindfulness meditation, relaxation response (RR), and cognitive behavioral therapy (CBT) with Yoga in their management of chronic pain. The use of these practices is explored through history, literature, and research studies. Results from scientific studies on Yoga show changes in health-related pain outcomes for patients with chronic pain. The key aspects of Yoga, notably relaxation, positive thinking, and mindfulness, are discussed in relation to mindfulness meditation, RR, and CBT.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0070.006
Open science0.0010.005
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.115
GPT teacher head0.335
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2022
Admission routes1
Has abstractyes

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