A Multi-Site, Randomized, Parallel-Group, Controlled Trial of Virtually-Delivered Sahaj Samadhi Meditation for the Management of Moderate Depressive Symptoms in Chronic Pain
Bibliographic record
Abstract
Background: Chronic pain (CP) often co-occurs with depression, but promising scalable interventions have been under-investigated. We assessed the effectiveness of the virtually-delivered Sahaj Samadhi Meditation (SSM) program in reducing depressive symptoms in people with CP and moderate depressive symptoms. Methods: We conducted a randomized controlled trial comparing SSM to the Health Enhancement Program (HEP), an active control. Participants were recruited from multiple sites in the Greater Toronto Area and virtually. Both 12-week programs were delivered virtually in groups by appropriately trained facilitators. Depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9) at baseline, 12 weeks, and 24 weeks. ClinicalTrials.gov registration number: NCT04039568. Results: Of 108 participants enrolled, 89 were randomized to SSM (n=43) or HEP (n=46). Between-group differences for the PHQ-9 were not significant. Within-group mean differences for SSM were significant and greater than the minimal clinically important difference at both 12 weeks and 24 weeks (-3.97 (95% CI -6.69 to -1.24) and -4.96 (-8.36 to -1.56), respectively), while within-group mean differences were not significant for HEP. Conclusion: This study suggests potential benefits of SSM for individuals with comorbid CP and depression. Future trials should include larger sample sizes in non-pandemic conditions to better evaluate the effectiveness of SSM. Further research should also explore pragmatic trial designs and the integration of mind-body interventions in clinical settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".