Virtually-delivered Sudarshan Kriya Yoga (SKY) for Canadian veterans with PTSD: A study protocol for a nation-wide effectiveness and implementation evaluation
Bibliographic record
Abstract
BACKGROUND: Post-traumatic stress disorder (PTSD) remains a significant treatment challenge among Canadian veterans. Currently accessible pharmacological and non-pharmacological interventions for PTSD often do not lead to resolution of PTSD as a categorical diagnosis and have significant non-response rates. Sudarshan Kriya Yoga (SKY), a complementary and integrative health (CIH) intervention, can improve symptoms of PTSD. In response to the COVID-19 pandemic, this intervention has pivoted to virtual delivery and may be reaching new sets of participants who face multiple barriers to care. OBJECTIVE: To evaluate the implementation and effectiveness of virtually delivered Sudarshan Kriya Yoga (SKY) on decreasing PTSD symptom severity, symptoms of depression, anxiety, and pain, and improving quality of life in Canadian veterans affected by PTSD. METHODS AND ANALYSIS: Using a mixed-methods approach guided by the RE-AIM framework, we will conduct a hybrid type II effectiveness and implementation study of virtually delivered Sudarshan Kriya Yoga (SKY) for Canadian veterans. Effectiveness will be evaluated by comparing virtually delivered SKY to a waitlist control in a single-blinded (investigator and data analyst) randomized controlled trial (RCT). Change in PTSD symptoms (PCL-5) is the primary outcome and quality of life (SF-36), symptoms of depression (PHQ-9), anxiety (GAD-7), and pain (BPI) are secondary outcomes. The SKY intervention will be conducted over a 6-week period with assessments at baseline, 6-weeks, 12-weeks, and 30 weeks. The reach, effectiveness, adoption, implementation, and maintenance of the intervention will be evaluated through one-on-one semi-structured interviews with RCT participants, SKY instructors, health professionals, and administrators that work with veterans. DISCUSSION: This is the first investigation of the virtual delivery of SKY for PTSD in veterans and aims to determine if the intervention is effective and implementable at scale.
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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 | 0.044 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".