Evaluating a group-based Yoga of Stress Resilience programme: a pragmatic before–after interventional study protocol
Bibliographic record
Abstract
INTRODUCTION: Rates of mental health illnesses and burnout are increasing internationally. Therapeutic yoga is increasingly used to improve and maintain physical, mental and emotional well-being and general health. This protocol describes a study to evaluate the effectiveness of an existing primary care group-based therapeutic yoga programme, the Yoga of Stress Resilience programme, which combines yoga and psychotherapeutic techniques, in improving mental health and decreasing burnout. Implementation factors will also be evaluated for potential scale-up. METHODS AND ANALYSIS: A pragmatic before-after interventional trial design will be used to study changes in occupational participation and mental health outcomes, including anxiety, depression, burnout, functional impairment, insomnia, perceived stress, loneliness, self-compassion and readiness for change in adults experiencing anxiety and burnout. Repeated measures analysis of variance will be used to determine changes in outcome measures over time. Regression and multivariate analyses will be conducted to examine relationships between participant characteristics and outcomes and among various outcomes. The Reach, Effectiveness, Adoption, Implementation, and Maintenance framework will be used to guide the analyses. ETHICS AND DISSEMINATION: Approval from the Hamilton Integrated Research Ethics Board has been waived: project number 7082 (full review waived). Informed consent will be obtained prior to enrolling any participant into the study. All data will be kept confidential. Peer-reviewed publications and presentations will target researchers and health professionals. TRIAL REGISTRATION NUMBER: The ClinicalTrials.gov registry (NCT03973216).
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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 | 0.038 | 0.028 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.012 |
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".