MétaCan
Menu
Back to cohort

Yoga for chronic pain management: a qualitative exploration

2010· article· en· W1707727056 on OpenAlexafffund
Yvonne Tul, Anita Unruh, Bruce Dick

Bibliographic record

VenueScandinavian Journal of Caring Sciences · 2010
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of AlbertaDalhousie University
FundersCanadian Institutes of Health ResearchDalhousie UniversityUniversity of Alberta
KeywordsChronic painQualitative researchPsychotherapistMedicinePain managementPsychologyPhysical therapySociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore patients' perceptions of their pain while participating in a weekly yoga program. METHODS: A consecutive convenience sample was recruited from a Multidisciplinary Pain Centre. Seven adult patients (six women), agreed to participate in an 8-week Hatha yoga program, including weekly group sessions and at-home practice. Data were gathered from participant observation and in-depth interviews. Interviews explored the experience of practicing yoga and its relationship to the participant's pain experience. An inductive analysis of the interviews explored emergent themes from participants' descriptions of their experience. RESULTS: Analyses identified three themes: renewed awareness of the body; transformed relationship with the body in pain; and acceptance. DISCUSSION: Participants' data suggested that they reframed what it meant to live with chronic pain. Some participants reported that the sensory aspects of pain did not change but that pain became less bothersome. They were better able to control the degree to which pain interfered with their daily life. Other participants reported less frequent or less intense pain episodes because they could recognize body signals and adjust themselves to alleviate painful sensations. The findings suggest that patients who benefit from yoga may do so in part because yoga enables changes in cognitions and behaviours towards pain.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.641
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0010.000

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 head0.077
GPT teacher head0.422
Teacher spread0.346 · 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 designTheoretical or conceptual
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

Citations52
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueScandinavian Journal of Caring SciencesSame topicMindfulness and Compassion InterventionsFrench-language works237,207