Implementation considerations of key knowledge users for building online mindfulness-based interventions for people with multiple sclerosis
Bibliographic record
Abstract
PURPOSE: Mindfulness-based interventions (MBIs) can effectively reduce stress in people with multiple sclerosis (PwMS). Online MBIs address access barriers, but large-scale implementation from the perspectives of key knowledge users remains understudied. This study explored the implementation considerations of PwMS, care partners, MS clinicians and MBI instructors for building online MBIs for PwMS. MATERIALS AND METHODS: = 8). An inductive thematic analysis approach was used. RESULTS: Four themes were identified: (1) daily mindfulness: structuring and conceptualizing mindfulness for PwMS, (2) unlocking access through enhanced clinician awareness and advocacy: building pathways to MBIs for PwMS, (3) validating mindfulness experiences: the importance of MBI group composition and instructor interactions for PwMS, and (4) sustained engagement: resources to create and navigate MBIs for PwMS. CONCLUSIONS: PwMS valued diverse participant groups and control over tailoring MBIs to their needs. However, guidance from a clinician may be needed to foster self-agency for PwMS. MBIs serve a multifaceted role for PwMS, extending beyond the diagnosis. Shared decision-making amongst knowledge users can enhance flexible programming of online MBIs.
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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 | 0.031 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".