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Record W2051075153 · doi:10.1186/1472-6882-12-s1-p60

P02.04. Internet survey confirms strong interest in Yoga among fibromyalgia patients

2012· article· en· W2051075153 on OpenAlexaboutno aff
Jerome Carson, Robert M. Bennett, Kim Dupree Jones, Scott Mist

Bibliographic record

VenueBMC Complementary and Alternative Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
Fundersnot available
KeywordsFibromyalgiaMedicineAlternative medicineTraditional medicineThe InternetFamily medicinePhysical therapyWorld Wide WebPathology

Abstract

fetched live from OpenAlex

We conducted a 16 question internet survey of persons self-identified as FM patients who were subscribers to electronic newsletters on the topic of FM. Respondents (N=2543) replied from all 50 U.S. states and also Canada, Australia and the United Kingdom. The average age of respondents was 57 years, 96% were female, and average time since diagnosis was 13 years. Of these, 80% had considered trying yoga and 58% had attended ≥1 yoga class. Their classes typically focused almost exclusively on yoga poses, with minimal training in meditation, breathing techniques or other practices. The most commonly cited benefits were reduced stiffness, relaxation, and better balance. The most frequently cited obstacles were fear that the poses would cause too much pain, concerns about the poses being too physically demanding, or not being able to do the poses correctly. These findings confirm strong interest in yoga across a geographically diverse range of FM patients. However, concerns about yoga-induced pain and yoga poses being too difficult are common reasons that FM patients do not engage in yoga exercises. This study supports the need for yoga programs tailored for FM that include modification of poses to minimize aggravating movements, and substantive training in meditation and other yoga-based coping methods to minimize pain-related fear.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.147
GPT teacher head0.358
Teacher spread0.211 · 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.

Study designObservational
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
Published2012
Admission routes1
Has abstractyes

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