Understanding and Preventing Yoga Injuries
Bibliographic record
Abstract
To obtain an initial estimate of the extent, nature, and causes of Yoga-related injuries, we invited 33,000 Yoga teachers, Yoga therapists, and other clinicians to participate in a 22-question survey. The survey was conducted with the cooperation of the International Association of Yoga Therapists (IAYT), Yoga Alliance, and Yoga Spirit. 1,336 responses came from 34 countries between May and October of 2007. A majority of participants believed that the most common and the most severe injuries occurred in the neck, the lower back, the shoulder and wrists, and the knee. Poor technique or alignment, previous injury, excess effort, and improper or inadequate instruction were the most commonly cited causes of Yoga injuries. Individual asanas were linked with particular injuries in a highly specific way. For example, neck injuries were attributed to sirsasana (headstand) and sarvangasana (shoulderstand); lower-back injuries were associated with forward bends, twists, and backbends; shoulder and wrist injuries were linked to adho mukha svanasana (downward-facing dog) and variations of plank pose (e.g., chaturanga dandasana, four-limbed staff pose and vasisthasana, side plank pose); and the knee was believed to be most frequently injured in virabhadrasana (warrior pose) I and II, virasana (hero's pose,) eka pada rajakapotasana (one-legged king pigeon pose) and padmasana (lotus pose).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".