Individual Differences in the Patient Experience of Relapsing Multiple Sclerosis (RMS): A Multi-Country Qualitative Exploration of Drivers of Treatment Preferences Among People Living with RMS
Bibliographic record
Abstract
AIMS: The aim of this study was to explore the experiences, values and preferences of people living with relapsing multiple sclerosis (PLwRMS) focusing on their treatments and what drives their treatment preferences. METHODS: In-depth, semi-structured, qualitative telephone interviews were conducted using a purposive sampling approach with 72 PLwRMS and 12 health care professionals (HCPs, MS specialist neurologists and nurses) from the United Kingdom, United States, Australia and Canada. Concept elicitation questioning was used to elicit PLwRMS' attitudes, beliefs and preferences towards features of disease-modifying treatments. Interviews with HCPs were conducted to inform on HCPs' experiences of treating PLwRMS. Responses were audio recorded and transcribed verbatim and then subjected to thematic analysis. RESULTS: Participants discussed numerous concepts that were important to them when making treatment decisions. Levels of importance participants placed on each concept, as well as reasons underpinning importance, varied substantially. The concepts with the greatest variability in terms of how much PLwRMS found them to be important in their decision-making process were mode of administration, speed of treatment effect, impact on reproduction and parenthood, impact on work and social life, patient engagement in decision making, and cost of treatment to the participant. Findings also demonstrated high variability in what participants described as their ideal treatment and the most important features a treatment should have. HCP findings provided clinical context for the treatment decision-making process and supported patient findings. CONCLUSIONS: Building upon previous stated preference research, this study highlighted the importance of qualitative research in understanding what drives patient preferences. Characterized by the heterogeneity of the RMS patient experience, findings indicate the nature of treatment decisions in RMS to be highly individualized, and the subjective relative importance placed on different treatment factors by PLwRMS to vary. Such qualitative patient preference evidence could offer valuable and supplementary insights, alongside quantitative data, to inform decision making related to RMS treatment.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".