Sahaj Samadhi Meditation versus a Health Enhancement Program for depression in chronic pain: protocol for a randomized controlled trial and implementation evaluation
Bibliographic record
Abstract
BACKGROUND: Despite the high prevalence of comorbid chronic pain and depression, this comorbidity remains understudied. Meditation has demonstrated efficacy for both chronic pain and depression independently, yet there have been few studies examining its effectiveness when both conditions are present concurrently. Furthermore, while meditation is generally accepted as a safe and effective health intervention, little is known about how to implement meditation programs within or alongside the health care system. METHODS: We will conduct a hybrid type 1 effectiveness-implementation evaluation. To measure effectiveness, we will conduct a randomized controlled trial comparing Sahaj Samadhi Meditation and the Health Enhancement Program in 160 people living with chronic pain, clinically significant depressive symptoms, and on long-term opioid therapy. Changes in depressive symptoms will be our primary outcome; pain severity, pain-related function, opioid use, and quality of life will be the secondary outcomes. The primary end point will be at 12 weeks with a secondary end point at 24 weeks to measure the sustainability of acute effects. Patients will be recruited from a community-based chronic pain clinic in a large urban center in Mississauga, Canada. The meditation program will be delivered in the clinical environment where patients normally receive their chronic pain care by certified meditation teachers who are not regulated health care providers. We will use a mixed-methods design using the multi-level framework to understand the implementation of this particular co-location model. DISCUSSION: Results of this hybrid evaluation will add important knowledge about the effectiveness of meditation for managing depressive symptoms in people with chronic pain. The implementation evaluation will inform both effectiveness outcomes and future program development, scalability, and sustainability. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04039568. Registered on 31 July 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".