Building Bridges: Establishing a Multiple Sclerosis Rehabilitation Research and Clinical Knowledge Mobilization Strategy
Bibliographic record
Abstract
BackgroundEvidence to guide multiple sclerosis (MS) rehabilitation and symptomatic care has grown, yet suboptimal access to care persists and uptake of evidence-based information is limited in practice. The movement of evidence into routine clinical care is not a spontaneous or linear process. Effective knowledge mobilization strategies may enhance equitable access to evidenced-based comprehensive MS care.MethodsTo guide the development of a MS rehabilitation knowledge mobilization strategy with priorities and action items a Canadian summit was hosted to engage key stakeholders in identifying and discussing current MS rehabilitation and symptomatic care evidence and needs. This multifaceted summit included workshops, breakout groups, presentations, brainstorming, and consensus-building.ResultsForty-three key stakeholders participated. Varied disciplines, Canadian geographical regions, and content expertise were represented. This included early/mid/late-career researchers, healthcare providers, and people with MS. The summit process identified 18 key need statements. Participants individually rated the identified need statements on feasibility and importance, and the relationships in terms of timeliness and impact were discussed. The three top priorities were identified and focused on for action planning. Developing a best-practice guideline for MS rehabilitation was unanimously identified as the critical first step to improve access to care. Support for healthcare providers and establishing a network to support this knowledge mobilization work were the next two priorities. Priority topic areas for knowledge mobilization were fatigue, mobility, cognition, mood and emotion, and rehabilitation across the MS disease course.ConclusionKnowledge mobilization priorities and key topic areas for MS rehabilitation have been identified using a collaborative process. The lessons learned from this summit will inform advocacy efforts for improved access to evidence-based comprehensive care and opportunities to support moving a sustainable MS rehabilitation knowledge mobilization agenda forward. Creating a formalized Canadian MS Rehab Knowledge Mobilization Network was an outcome of the summit, and our network will collaboratively support advancing and re-evaluating this agenda.
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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 | 0.235 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.010 | 0.051 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".