Multiple sclerosis caregiving: A systematic scoping review to map current state of knowledge
Bibliographic record
Abstract
Unpaid caregivers, typically family and friends, provide significant amounts of support to people with multiple sclerosis (MS). Understanding their experiences, needs and challenges is necessary to ensure that caregivers receive the support that they require to continue in their role. Our aim was to map the current state of knowledge about unpaid caregivers of people with MS and identify gaps in knowledge to guide future research and practice. We used scoping review methodology with three major health-related databases (MEDLINE, CINAHL, APA Psychinfo), searching in September 2019, April and October 2020 and October 2021. We selected peer-reviewed scientific articles reporting on primary studies of unpaid caregivers of people with MS, regardless of topic or research design. We extracted information on study aim, participant characteristics, measures used and key findings to generate major themes and identify knowledge gaps. We identified 108 published studies between 1992 and 2021 that met our criteria. Studies of spousal caregivers were most common. Studies focused primarily on measurement of caregiver burden or other negative consequences of caregiving. Thirteen studies addressed positive consequences of caregiving. Sixteen studies reported actual tasks performed by caregivers and seven reported outcomes of caregiver support interventions. Attention to diversity issues that may influence caregiving experiences and outcomes was rare. Overall, knowledge of MS caregiving is limited, particularly with respect to tasks performed by caregivers that may contribute to negative outcomes, diversity issues and effective approaches to remediate caregiver burden. Without this knowledge, finding ways to better support MS caregivers will be difficult.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".