The Relationship between Falls Efficacy and Improvement in Fall Risk Factors Following an Exercise Plus Educational Intervention for Older Adults with Hip Osteoarthritis
Bibliographic record
Abstract
PURPOSE: Older adults with decreased confidence in their ability to prevent a fall may benefit from an exercise programme that includes self-efficacy-enhancing education. The objectives of this study were to explore differences in fall-risk outcomes in older adults with higher vs. lower levels of falls efficacy and to evaluate the relationship between baseline falls-efficacy status and changes in fall risk factors following two interventions. METHOD: Fifty-four older adults with hip osteoarthritis and at least one risk factor for falls received aquatic exercise twice weekly plus education once weekly (EE) or aquatic exercise only, twice weekly (EO), for 11 weeks. RESULTS: EE participants with low baseline falls efficacy demonstrated significantly (p<0.05) greater improvement in balance and falls efficacy compared to EE participants with high baseline falls efficacy. In the EE group only, baseline falls-efficacy status (low vs. high median split on the Activities-specific Balance Confidence Scale) was significantly (p<0.05) correlated with positive balance and falls-efficacy change scores (Spearman rank r=0.45 and 0.63 respectively). CONCLUSIONS: Individuals with one or more fall-risk factors and low falls efficacy may benefit from receiving an intervention that combines exercise with self-efficacy-enhancing education. Falls-efficacy screening may be important for decisions regarding referral to fall-prevention programmes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".