Adaptive Interventions for Enhancing Participation Poststroke: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background. Stroke rehabilitation includes both restorative and adaptive interventions. There are few specific guidelines regarding adaptive interventions. Purpose. To determine the effectiveness of adaptive interventions on participation outcomes generally, and more specifically by theoretical focus of the intervention. Method. Systematic review. Relevant databases were searched for randomized controlled trials of adaptive interventions that included a participation measure. Data were extracted and Hedges’ g was calculated for all interventions and then by intervention classification. Results. Fourteen named interventions were examined in 24 studies. When all interventions were considered, the following was observed: a medium effect on person-specific participation goals that approached significance (0.60; [95% CI: −0.06; 1.26], p = .07), a negligible and non-significant effect on broad participation (0.10; [95% CI: −0.13; 0.33], p = .37), and a small to medium significant effect on instrumental activities of daily living (IADL; 0.37, [95% CI: 0.12; 0.62] p = .004). Studies evaluating person-specific participation were dominated by learning-focused interventions, while studies evaluating broad participation and IADL were dominated by coping-focused interventions. Conclusion. Learning- or motivation-focused approaches appear to have an important impact on person-specific participation goals. Future research should focus on clarifying the effectiveness of these interventions and improving impact on broader 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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".