Adherence to disease modifying therapies (DMTs) in multiple sclerosis: a thematic meta-synthesis of qualitative research
Bibliographic record
Abstract
Up to 59% of people with multiple sclerosis (MS) are sub-optimally adherent to disease modifying therapies (DMTs), leading to relapses and hospitalization. The aim of this review is to systematically identify, appraise and synthesise qualitative research exploring views and experiences of people with MS regarding DMTs, to identify factors potentially influencing treatment adherence. Systematic searches of six databases and citation searching identified 1326 unique citations. Screening by two reviewers yielded 12 studies for inclusion. These were appraised using the Critical Appraisal Skills Programme tool and synthesised thematically. Included papers dated from 2001-2014, reporting studies conducted in the USA (n=8), Canada (n=3) and the UK (n=1). The majority focused on experience of self-injecting DMTs, though one study examined only intravenous infusions and four additionally considered oral therapies. Synthesis generated higher-order analytical themes leading to broader conceptual understandings than presented within individual studies. Themes encompassed the importance of feeling in control; the desire to lead a normal life and the extent to which DMTs facilitated or inhibited this; and the continual process of weighing up costs and benefits of treatment (including adverse side effects and perceived effectiveness) to achieve optimal quality of life now and in the future. People with MS take DMTs when they believe the drugs will facilitate their living a ‘normal’ life, and avoid taking them when they believe they will interfere with quality of life or present risks. Future research should investigate adherence to oral and intravenous DMTs, and interventions should address these patient objectives.
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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 | 0.133 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".