Rehabilitation Interventions for Improving Social Participation After Stroke
Bibliographic record
Abstract
BACKGROUND: Despite the fact that social participation is considered a pivotal outcome of a successful recovery after stroke, there has been little attention on the impact of activities and services on this important domain. OBJECTIVE: To present a systematic review and meta-analysis from randomized controlled trials (RCTs) on the effects of rehabilitation interventions on social participation after stroke. METHODS: A total of 8 electronic databases were searched for relevant RCTs that evaluated the effects of an intervention on the outcome of social participation after stroke. Reference lists of selected articles were hand searched to identify further relevant studies. The methodological quality of the studies was assessed using the Physiotherapy Evidence Database Scale. Standardized mean differences (SMDs) and confidence intervals (CIs) were estimated using fixed- and random-effect models. RESULTS: In all, 24 RCTs involving 2042 stroke survivors were identified and reviewed, and 21 were included in the meta-analysis. There was a small beneficial effect of interventions that utilized exercise on social participation (10 studies; SMD = 0.43; 95% CI = 0.09, 0.78;P= .01) immediately after the program ended. Exercise in combination with other interventions (13 studies; SMD = 0.34; 95% CI = 0.10, 0.58;P= .006) also resulted in beneficial effects. No significant effect was observed for interventions that involved support services over 9 studies (SMD = 0.09 [95% CI = -0.04, 0.21];I(2)= 0%;P= .16). CONCLUSIONS: The included studies provide evidence that rehabilitation interventions may be effective in improving social participation after stroke, especially if exercise is one of the components.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.016 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".