Pharmacist-Implemented Self-Management Module in Multiple Sclerosis Patients: A Randomized Controlled Trial
Bibliographic record
Abstract
ABSTRACT Background: Self-management practices can contribute to the lives of patients with multiple sclerosis. The aim of this study is to improve patients’ self-management abilities through a multidisciplinary developed module. Methods: This prospective, randomized controlled trial was conducted between January 2020 and November 2021 at a university hospital in Ankara, Turkiye. The self-management module was implemented by a clinical pharmacist with the aim of enhancing self-management capabilities through an educational approach, with a focus on medication adherence, management of drug-related problems, follow-ups and self-directed activities. The intervention group completed the self-management module, while the control group received usual outpatient care. To evaluate the impact of the module, the Multiple Sclerosis Self-Management Revised scale was administered to the patients. Interviews were conducted at 4-month intervals. Results: Study (n = 102) and control group (n = 98) patients were followed up for 8 months, and the median duration of intervention was 11 minutes. The mean (± SD) self-management scores of the study group increased from 68.9 (± 9.3) to 79.0 (± 9.4) at the end of the interviews, and this increase was found to be significant compared to the control group (p < 0.001). The self-management module has been shown to improve self-management, medication adherence, perception of care and patient engagement in treatment (p < 0.001). Conclusions: This single-center randomized controlled trial suggests that a pharmacist-implemented self-management module increased patient engagement and medication adherence. The self-management interventions could be tailored to groups that tend to have lower self-management abilities, such as older individuals, and those who have lower educational attainment, health engagement or medication adherence.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".