Multifactorial and functional mobility assessment tools for fall risk among older adults in community, home-support, long-term and acute care settings
Bibliographic record
Abstract
OBJECTIVE: to conduct a systematic review of published studies that test the validity and reliability of fall-risk assessment tools for use among older adults in community, home-support, long-term and acute care settings. METHODS: searches were conducted in EbscoHost and MEDLINE for published studies in the English language between January 1980 and July 2004, where the primary or secondary purpose was to test the predictive value of one or more fall assessment tools on a population primarily 65 years and older. The tool must have had as its primary outcome falls, fall-related injury or gait/balance. Only studies that used prospective validation were considered. FINDINGS: thirty-four articles testing 38 different tools met the inclusion criteria. The community setting represents the largest number of studies (14) and tools (23) tested, followed by acute (12 studies and 8 tools), long-term care (LTC) (6 studies and 10 tools) and home-support (4 studies and 4 tools). Eleven of the 38 tools are multifactorial assessment tools (MAT) that cover a wide range of fall-risk factors, and 27 are functional mobility assessment tools (FMA) that involve measures of physical activity related to gait, strength or balance. CONCLUSION: fall-risk assessment tools exist that show moderate to good validity and reliability in most health service delivery areas. However, few tools were tested more than once or in more than one setting. Therefore, no single tool can be recommended for implementation in all settings or for all subpopulations within each setting.
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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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".