Mindfulness-based interventions for people with multiple sclerosis: a systematic review and meta-aggregation of qualitative research studies
Bibliographic record
Abstract
PURPOSE: Mindfulness-based interventions (MBIs) are effective treatments for stress, anxiety, and depression in PwMS. However, low adherence and high attrition may limit effectiveness. Qualitative research can provide important insights into MBI acceptability, accessibility, and implementation. This systematic review and meta-aggregation evaluated qualitative research findings on the use of MBIs for PwMS. METHODS: Systematic searches were undertaken in six major electronic databases. Studies using qualitative or mixed methods were included. Two reviewers screened, data extracted, and critically appraised studies. Meta-aggregation was performed following the Joanna Briggs Institute approach, extracting findings, developing categories, and synthesizing findings. RESULTS: Six eligible papers, including 136 PwMS were included in meta-aggregation. 136 findings were extracted, grouped into 17 categories, with four synthesized findings: (1) "accessing mindfulness," (2) "a sense of belonging," (3) "experiencing mindfulness," and (4) "making mindfulness more relevant and sustainable for PwMS." CONCLUSIONS: MBIs for PwMS need to take into consideration disability which can limit accessibility. Online MBIs (synchronous and asynchronous) appear acceptable alternatives to traditional face-to-face courses. However, PwMS benefit from shared (mindful) experiencing and highlight MBI instructors as crucial in helping them understand and practice mindfulness. Involving PwMS in design, delivery, and iterative refinement would make MBIs more relevant to those taking part.IMPLICATIONS FOR REHABILITATIONBoth face-to-face and online Mindfulness-based interventions (MBIs) appear acceptable to PwMS and, ideally, people should be offered a choice in training modality.PwMS derive benefit from undertaking MBIs with their peers, where a sense of camaraderie and belonging develop through shared (mindful) experiences.Instructors delivering MBIs for PwMS should be knowledgeable about the condition; participants describe how the instructor has a key role in helping them practice mindfulness effectively in the context of unpleasant experiences associated with MS.MBIs tailored for PwMS should include a pre-course orientation session, clearly articulate how mindfulness practices can help with MS, provide well-organized course materials in large font size, and deliver individual mindfulness practices flexibly depending on participant need.
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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 | 0.079 | 0.153 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".