Challenges in multiple sclerosis care: Results from an international mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Disease-modifying treatment (DMT) selection for people with multiple sclerosis (MS) is challenging. Neurologists and advanced practice nurses (APNs) in MS care may be facing knowledge and confidence gaps when screening patients to initiate or switch between DMTs, assessing the safety of new DMTs and monitoring for adverse events. Healthcare providers are required to demonstrate enhanced patient communication skills, to share treatment decisions and assess treatment adherence. To better inform educational interventions, there is a need to better understand these challenges and uncover their causalities. We undertook an international study across seven countries to identify challenges for neurologists and APNs that may impact DMT choices and optimum care for people with MS (pwMS). METHODS: This mixed methods study involved two concurrent data collection phases, a qualitative phase with semi-structured interviews and a quantitative phase using an online survey. Neurologists (n=333) and APNs (n=135) were recruited from Canada, France, Germany, Italy, Spain, United Kingdom and the United States. All participants had to have a minimum of two years' experience in the care of pwMS and be currently active in clinical practice. RESULTS: A triangulated analysis of qualitative and quantitative data identified multiple challenges. For APNs, these mainly related to diagnosing MS, integrating new agents in their practice, sequential DMT selection, treatment monitoring and providing personalized care. Specifically, two-thirds of APNs reported no or basic knowledge of the 2017 McDonald criteria and over half reported a knowledge gap of new DMTs available (51%) and a skill gap when integrating them into practice (58%). APNs expressed a knowledge gap of treatment sequencing (46%) and a skill gap in making decisions about sequencing (62%). Forty-four percent of APNs reported a gap in their skills of integrating patient's goals into treatment recommendations. For neurologists, the main challenges included managing side effects, aligning care to their patient's personal goals and quality of life (QoL). Specifically, over a third of neurologists reported no or basic knowledge of the characteristics of treatment failure (35%), and 32% reported no or basic skills identifying treatment failure. Skills needed to integrate patient's individual goals into treatment recommendations were reported as none or low by 39% of neurologists. In addition, there were significant differences according to years of practice in the majority (9 out of 14) of confidence items with respect to discussing specific MS-related topics with patients. Significant differences between countries were also identified. CONCLUSION: The complexity of diagnosing MS and the variety of available DMTs for pwMS lead to uncertainties, even among specialized healthcare professionals. These should be addressed through focused education and training to optimize care for pwMS.
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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 | 0.089 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".