Best Practices for Adapting and Delivering Community-Based Yoga for People with Traumatic Brain Injury in the United States and Canada
Bibliographic record
Abstract
Emerging benefits of yoga for traumatic brain injury (TBI) suggest that broader accessibility to community-based yoga programming is important. This cross-sectional, mixed methods study sought to identify best practices for adapting and delivering community-based yoga to people with TBI. An online survey was sent to 175 yoga teachers trained to teach LoveYourBrain Yoga, a community-based, 6-week, manualized program for people with TBI and their care-givers. The survey instrument included open- and closed-text questions assessing teachers' perspectives on the most and least helpful adaptions for asana, meditation, pranayama, and group discussion, and on the LoveYourBrain Yoga training and support. Responses we re analyzed using d e s c r i p t i ve statistics and qualitative content analysis. Eighty-six teachers (50%) responded. Best practices for adapting yoga for TBI revealed six themes: (1) simple, slow, and repeated; (2) creating a safe space; (3) position of the head and neck; (4) demonstration; (5) importance of props; and (6) special considerations for yoga studios. Three themes emerged for yoga program delivery: (1) structured yet flexible; (2) acceptability of compensation; and (3) time management. Eighty-nine percent of teachers reported that the program manual was very/extremely helpful, yet nearly half (49%) adapted the manual content often/always. To deliver community-based yoga services for TBI, we recommend an environment with props, low light and noise, and sufficient space, along with the facilitation of consistent instruction with a manual that allows for flexibility. We recommend that yoga teachers have skills in physical modifications for the head and neck; slow, simple, and repeated cueing to facilitate cognitive processing; managing challenging behaviors through redirection techniques; and promoting safety through inclusivity, compassion, and personal agency.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".