The use of self-management strategies for stroke rehabilitation: a scoping review
Bibliographic record
Abstract
Self-management is generally considered a dynamic and collaborative process by individuals and caregivers to manage a chronic condition. Self-management has recently emerged as a promising strategy for stroke rehabilitation. This scoping review aims to examine and summarize self-management strategies utilized by stroke survivors for stroke rehabilitation. PubMed, Scopus, CINAHL (EBSCO), Embase, and ProQuest were searched for articles published between January 2010 and December 2021. Studies were selected if they were published in English in a peer-reviewed journal, utilized a non-experimental research design, and focused on adult stroke survivors. All relevant information from the included articles was extracted in a systematic way using a pre-developed data extraction form. Two authors performed data extraction and quality evaluation independently. All issues were resolved through discussion among the authors. We narratively summarized the findings of 15 quantitative, qualitative, and mixed-method studies, including a total of 1,494 stroke survivors. The stroke survivors used a range of self-management strategies for their stroke rehabilitation, including domains related to lifestyle, social support, communication, knowledge and information, and goal-setting. Gender, age, stroke-related disability, fatigue, self-management education, social support, and communication with others were found to be associated with self-management use in stroke rehabilitation. This scoping review provides an important overview on stroke survivors’ use of self-management strategies and their experience. Their use of self-management strategies is complicated and multifaceted, comprising several domains and involving a diverse range of approaches and personal experiences. However, we identified several gaps in the literature and more research is required.
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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 | 0.024 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".