The effect of interventions on balance self-efficacy in the stroke population: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review of clinical trials that examined the effectiveness of interventions on balance self-efficacy among individuals with stroke. DESIGN: Systematic review. SUMMARY OF REVIEW: Searches of the following databases were completed in December 2014: MEDLINE (1948-present), CINAHL (1982-present), EMBASE (1980-present) and PsycINFO (1987-present) for controlled clinical trials that measured balance self-efficacy in adults with stroke. Reference lists of selected articles were hand-searched to identify further relevant studies. REVIEW METHODS: Two independent reviewers performed data extraction and assessed the methodological quality of the studies using the Physical Therapy Evidence Database Scale. Standardized mean differences (SMD) were calculated. RESULTS: A total of 19 trials involving 729 participants used balance self-efficacy as a secondary outcome. Study quality ranged from poor (n = 3) to good (n = 8). In the meta-analysis of 15 trials that used intensive physical activity interventions, a moderate beneficial effect on balance self-efficacy was observed immediately following the programs (SMD 0.44, 95% CI 0.11-0.77, P = 0.009). In the studies that included follow-up assessments, there was no difference between groups across retention periods (eight studies, SMD 0.32, 95% CI -0.17-0.80, P = 0.20). In the four studies that used motor imagery interventions, there was no between-group difference in change in balance self-efficacy (fixed effects SMD 0.68, 95% CI -0.33-1.69, P = 0.18). CONCLUSIONS: Physical activity interventions appear to be effective in improving balance self-efficacy after stroke.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.031 | 0.033 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".