Supportive Interventions for Caregivers of Individuals With Multiple Sclerosis: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Caregivers of individuals with multiple sclerosis (MS) are key members of the treatment team. Their needs and challenges should be met as interventions can be effective in improving not only their own health, well-being, and quality of life but also that of those they care for. The aim of this systematic review was to investigate supportive interventions for caregivers of individuals with MS. METHODS: We conducted a database search of PubMed, Google Scholar, Science Direct, Scopus, and the Cochrane Library from 2000 to 2021. English-language studies that examined interventions administered directly to caregivers of individuals with MS and evaluated various outcomes were included. The Downs and Black checklist was used to assess the methodological quality of included studies. RESULTS: Twenty of 367 relevant papers fit the eligibility criteria outlined in the methods of this study and were subsequently selected for this review. Of the included studies, there was a notable variance in key characteristics such as methods, outcome measures, sample size, and procedures. Supportive interventions, psychoeducational group interventions, and behavioral-adaptive therapies were the 3 main categories of interventions reviewed; however, each study had a significant correlation between the intervention and outcomes. CONCLUSIONS: Despite the small sample size in this study, this review showed that various intervention models that target caregivers of individuals with MS have been successful.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".