Experiences of Veteran and Civilian Patients in Exploratory Yoga Sessions for Chronic Pain: A Qualitative Study
Bibliographic record
Abstract
Background: Yoga integrates all aspects of self, with biological, mental, intellectual, and spiritual elements. The practice of yoga aligns with the biopsychosocial model of health and, as such, it can be instrumental in pain treatment. Aims: The purpose of this qualitative study was to explore perceptions regarding the yoga sessions for chronic pain through thematic content analysis with comparison of gender, veteran or civilian status, and delivery methods. Methods: Patients with chronic pain attended a 5-week intensive interdisciplinary chronic pain management program at the Michael G. DeGroote Pain Clinic. Participants were asked to complete six open-ended questions following four weekly 1-h yoga classes, through in-person or virtual delivery. Survey responses were thematically and separately analyzed by reviewers. Results: = 41) participants (56% males, 71% veterans) with an average age of 50.87 (SD 10.10) years provided comments. Nine themes emerged: (1) mind and body are one through yoga practices; (2) meaningful practice of yoga basics is productive for range of motion/movement, tension in joints, and chronic pain; (3) yoga classes provide an enjoyable process of learning; (4) yoga reminds patients of their physical capabilities; (5) routine practices lead to improvements; (6) yoga improved on strategies for chronic pain; (7) yoga can be adapted for each patient; (8) mindset improves to include positive thinking, better focus, and willingness to try new things; and (9) improvements exist for the current yoga programming. Conclusion: Findings of the current study were nine qualitative themes that present the experience of patients with chronic pain in the yoga sessions.
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 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".