Evaluation of a balance and mobility program for older adults at risk of falling: a mixed methods study
Bibliographic record
Abstract
RATIONAL, AIMS, AND OBJECTIVES: The FallProof Balance and Mobility Program is a multifactorial fall prevention intervention that targets intrinsic risk factors such as muscle strength, balance, gait, and posture. Using mixed methods, we evaluated the implementation of the program for older adults at high risk of falling in the community. METHODS: A pre-post program evaluation and semi-structured interviews were used to evaluate FallProof Balance and Mobility Program offered to older adults who were recurrent fallers. Over a 1-year period, the 12-week program was offered five times. Feasibility, acceptability, and outcome evaluation along with semi-structured interviews were done. Over the course of the evaluation, participants were evaluated three times (baseline, 12, and 16 weeks). RESULTS: Of the 19 participants, who enrolled in the program, 16 completed the program and 12 attended at least 80% of the classes. Fourteen participants had mildly impaired cognition (Montreal Cognitive Assessment <26). Large gains (effect size 0.90) were seen with self-management (Partner-in-Health Scale). Participants were very satisfied with the program. Three themes emerged from the semi-structured interviews: (a) fall-related benefits, (b) variety of activities and motivating instructors, and (c) deterrents to participation. CONCLUSION: Findings provided insights into pragmatic issues of implementing a balance and mobility program for older adults at risk of falling. The FallProof program was found to be feasible and acceptable in a small cohort of older adults from the community.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".