Virtual Mindfulness-Based Therapy for the Management of Endometriosis Chronic Pelvic Pain: A Novel Delivery Platform to Increase Access to Care
Bibliographic record
Abstract
OBJECTIVES: This study assessed the effectiveness of a virtual mindfulness-based stress reduction (MBSR) program to improve quality of life and pain in people with endometriosis. METHODS: This was a multiple-method, before and after study design. Fifteen patients with a clinical or surgical diagnosis of endometriosis were recruited from a Canadian outpatient gynaecology clinic. Participants completed the Endometriosis Health Profile, a validated survey tool, and a pain medication use questionnaire before and after a virtual 8-week MBSR program run by an experienced social worker. A focus group was held upon completion of the program to assess participants' experiences using mindfulness for management of endometriosis symptoms. Quantitative data was analyzed with paired-samples t tests. Qualitative data was thematically analyzed. RESULTS: A total of 67% of people enrolled completed the MBSR course (10/15). Following the MBSR program, participants had a statistically significant decrease in 4 components of the Endometriosis Health Profile: control and powerlessness (P = 0.012), emotional well-being (P = 0.048), social support (P = 0.030), and self-image (P = 0.014). There was no change in pain scores or medication use. Participants felt the program's benefits came from a sense of community, education about their condition, and application of mindfulness tools when approaching pain. Participants felt more comfortable with the virtual format over in-person sessions. CONCLUSIONS: A virtual MBSR course can improve quality of life domains in people with endometriosis. The virtual format was effective and preferred by participants. Virtual MBSR programs may increase access to this type of care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 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".