Determinants of Satisfaction With Community Reintegration in Older Adults With Chronic Stroke: Role of Balance Self-Efficacy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Many people with stroke have a low level of satisfaction with community reintegration. Although previous studies focused on the effect of physical factors on community reintegration, the effect of psychological factors, such as balance self-efficacy, has been ignored. The purpose of this study was to determine the contribution of balance self-efficacy to satisfaction with community reintegration in older adults with chronic stroke. SUBJECTS: A sample of 63 community-dwelling older adults (50 years of age or older) with chronic stroke (onset of 1 year or more) participated in this study. METHODS: This study involved a secondary analysis of data collected from a stroke exercise clinical trial. Satisfaction with community reintegration was measured with the Reintegration to Normal Living (RNL) Index, and balance self-efficacy was measured with the Activities-specific Balance Confidence (ABC) Scale. RESULTS: Bivariate correlation analyses showed that the RNL Index scores were moderately correlated with the ABC Scale scores. In a multiple regression analysis, after adjusting for age, sex, depression, and other impairments after stroke, balance self-efficacy remained independently associated with the RNL Index scores, accounting for 6.5% of the variance in the RNL Index scores. DISCUSSION AND CONCLUSION: Balance self-efficacy is an independent predictor of satisfaction with community reintegration in older adults with chronic stroke. Improving balance self-efficacy may be instrumental in enhancing community reintegration in this population.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".