Dyad interventions for health-related quality of life, activity, and participation after stroke: a systematic review
Bibliographic record
Abstract
Purpose To characterize the effects of dyad interventions on stroke survivor and caregiver health-related quality of life (HRQOL), participation, and activity outcomes.Methods Five databases were searched using: stroke AND social support/dyad relationships AND intervention. No date or language restrictions were applied. Title/abstract/full-text review and data extraction were conducted by two independent raters. Studies that tested between-group or within-group effects of an intervention that engaged both dyad members together in at least one intervention session and measured at least one outcome of interest (HRQOL, participation, or activity) in both dyad members were included.Results Among 64,988 records, 401 full-text articles were reviewed, and 17 studies were included. Three dyad intervention types were identified: stroke education and caregiver training, joint psychosocial interventions, and caregiver-mediated exercise. Effects on HRQOL were mixed. Among five studies that favored the intervention group on HRQOL, effects ranged from moderate to large among stroke survivors (Cohen’s d = 0.51 to 7.03) and caregivers (Cohen’s d = 0.66 to 7.90). Measures of caregiver activity and participation were rarely included.Conclusions There is emerging evidence for certain types of dyad intervention after stroke. Future research should examine the effectiveness and mechanisms of these dyad interventions and include measures of caregiver activity and participation.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.006 | 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".