The Efficacy of Fall Hazards Identification on Fall Outcomes: A Systematic Review With Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate the efficacy of fall hazards identification programs when compared to no intervention or other fall prevention programs on number of falls, falls incidence, and identifying fall hazards in community-dwelling adults. DATA SOURCES: CINAHL, PubMed, EMBASE, Scopus, and PsychINFO were used to identify articles. STUDY SELECTION: Studies were selected to compare fall hazards identification programs to a control group. Studies were eligible if they were randomized controlled trials and enrolled adults older than 50 years with the incidence rate of falls as an outcome. DATA EXTRACTION: Study or authors, year, sample characteristics, intervention or comparison groups, number of falls, and number of hazards identified in the intervention and control groups, and follow-up were extracted. The risk of bias assessment was performed using the Cochrane Risk of Bias tool. Quality was evaluated with Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach per outcome. DATA SYNTHESIS: A total of 8 studies (N=8) and 5177 participants were included. There was a high risk of bias across the studies mostly due to improper blinding of personnel of the outcome assessor. Pooled estimate effects from 5 studies assessing the incidence rate of falls from 3019 individuals indicated no difference between fall hazards identification programs and control (incidence rate ratio=0.98; 95% confidence interval, 0.87-1.10). CONCLUSIONS: The current study suggests that there may be a benefit for fall hazards programs in reducing incident falls. However, because of a moderate GRADE rating, more large-scale studies with a higher number of falls events and more consistent control groups are required to determine the true effect.
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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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.045 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".