Achieving patient engagement in multiple sclerosis: A perspective from the multiple sclerosis in the 21st Century Steering Group
Bibliographic record
Abstract
While advances in medicine, technology and healthcare services offer promises of longevity and improved quality of life (QoL), there is also increasing reliance on a patient׳s skills and motivation to optimize all the benefits available. Patient engagement in their own healthcare has been described as the 'blockbuster drug of the century'. In multiple sclerosis (MS), patient engagement is vital if outcomes for the patient, society and healthcare systems are to be optimized. The MS in the 21st Century Steering Group devised a set of themes that require action with regard to patient engagement in MS, namely: 1) setting and facilitating engagement by education and confidence-building; 2) increasing the importance placed on QoL and patient concerns through patient-reported outcomes (PROs); 3) providing credible sources of accurate information; 4) encouraging treatment adherence through engagement; and 5) empowering through a sense of responsibility. Group members independently researched and contributed examples of patient engagement strategies from several countries and examined interventions that have worked well in areas of patient engagement in MS, and other chronic illnesses. The group presents their perspective on these programs, discusses the barriers to achieving patient engagement, and suggests practical strategies for overcoming these barriers. With an understanding of the issues that influence patient engagement in MS, we can start to investigate ways to enhance engagement and subsequent health outcomes. Engaging patients involves a broad, multidisciplinary approach.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".