Evidence Based Strategies for Preventing Falls in Community-Dwelling Older Adults
Bibliographic record
Abstract
Objective: To evaluate and summarize the best evidence for fall prevention measures in community-dwelling elderly individuals. Methods: A systematic search was conducted in databases including UpToDate, BMJ Best Practice, the Joanna Briggs Institute (JBI) Evidence-Based Healthcare Center, National Guidelines Clearinghouse, China Medical Guidelines Network, NICE, Scottish Intercollegiate Guidelines Network, and the Ontario Nurses' Association website. Additional searches were performed in the Cochrane Library, CINAHL, PubMed, Embase, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP databases for clinical decisions, guidelines, evidence summaries, systematic reviews, and expert consensus related to fall prevention in community-dwelling elderly individuals. The methodological quality of included literature was assessed, and evidence was extracted and synthesized based on key themes. Results: A total of 3 guidelines, 1 expert consensus, and 12 systematic reviews were included. Twenty-six pieces of evidence were summarized across 8 areas: fall risk screening, fall risk assessment, exercise interventions, medication management, environmental safety, health education, psychological interventions, and multifactorial interventions. Conclusion: The best evidence for fall prevention in community-dwelling elderly individuals was summarized, providing an evidence-based foundation for community healthcare providers to implement fall prevention measures.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".