Factors influencing older adult community fall prevention exercise implementation: a scoping review
Bibliographic record
Abstract
BACKGROUND: Exercise that challenges balance is the most effective fall prevention intervention in community-dwelling older adults. Identifying factors influencing implementation of community fall prevention exercise programs is a critical step in developing strategies to support program delivery. OBJECTIVE: To identify implementation facilitators, barriers, and details reported in peer-reviewed publications on community fall prevention exercise for older adults. DESIGN: Scoping review. METHODS: We searched multiple databases up to July 2023 for English-language publications that reported facilitators and/or barriers to implementing an evidence-based fall prevention exercise program in adults aged 50+ years living independently. At least two reviewers independently identified publications and extracted article, implementation, and exercise program characteristics and coded barriers and facilitators using the Consolidated Framework for Implementation Research (CFIR). RESULTS: We included 22 publications between 2001 and July 2023 that reported factors influencing implementation of 10 exercise programs. 293 factors were reported: 183 facilitators, 91 barriers, 6 described as both a facilitator and barrier, and 13 unspecified factors. Factors represented 33 CFIR constructs across all five CFIR domains: implementation inner setting (n = 95 factors); innovation (exercise program) characteristics (n = 84); individuals involved (n = 54); implementation process (n = 40) and outer setting (n = 20). Eight publications reported implementation strategies used; 6 reported using a conceptual framework; and 13 reported implementation outcomes. CONCLUSION: The high number of factors reflects the complexity of fall prevention exercise implementation. The low reporting of implementation strategies, frameworks and outcomes highlight the ongoing need for work to implement and sustain community fall prevention exercise programs.
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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 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".