Yoga for chronic pain management: a qualitative exploration
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".