“No Pain, More Gain”: Subjective Responses to a T’ai Chi and Qigong Program For Back Pain
Bibliographic record
Abstract
Background: Low back pain is the leading cause of disability worldwide. Multidisciplinary approaches to chronic pain can support resilience in recovery of functioning. The ancient arts of t'ai chi and qigong have been touted for centuries as multimodal approaches to health and healing. An RCT of an on-line t'ai chi and qigong program for individuals with chronic low back pain found clinically significant improvements in pain, physical function, and quality of life. The program consisted of the integrated practice of t'ai chi movement and the stillness of qigong practice. Meditations in the program focused on developing positive mental attitudes to support resilience in managing the emotional challenges of pain and disease. Objective: The purpose of the current qualitative study aimed to better understand participants' experiences during the online Heal and Strengthen the Spine with T'ai Chi and Qigong (HSSTQ) program. Methods: Written feedback was received from 96 program participants, and semi-structured interviews were completed with 20 participants. Interviews were transcribed and coded based upon the written feedback and interview questions. A thematic narrative was developed from the coded segments of texts and reviewed by participants in a member check. Results: Three interlocking themes characterized the experiences of participants in the program: (1) participants learned skills to relieve back pain including moderation in movement; (2) the holistic nature of the program enabled a range of benefits for participants beyond pain reduction; and (3) personal healing experience, the enthusiasm of the instructor, the program itself, and available staff resources motivated individuals to participate. Conclusion: The conclusion from this study and the initial RCT is that a virtually delivered integrated tai chi, qigong, and meditation program may be a viable treatment option for adults with low back 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".