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Record W1974713620 · doi:10.1097/pts.0b013e3181a551ed

Risk Factors for Falling Among Community-Based Seniors

2009· article· en· W1974713620 on OpenAlexaffabout
Paula C. Fletcher, Katherine Berg, Dawn M. Dalby, John P. Hirdes

Bibliographic record

VenueJournal of Patient Safety · 2009
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of WaterlooUniversity of TorontoWilfrid Laurier University
FundersU.S. Department of Veterans Affairs
KeywordsFalling (accident)MedicineFall preventionBerg Balance ScaleOccupational safety and healthGerontologyInjury preventionPoison controlSuicide preventionActivities of daily livingIntervention (counseling)Veterans AffairsBalance (ability)Human factors and ergonomicsRisk assessmentPhysical therapyEnvironmental healthNursingComputer security

Abstract

fetched live from OpenAlex

BACKGROUND: Falling constitutes a significant risk to the health and well-being of seniors. Although a number of risk factors have been established within the literature for falling, limited work has differentiated risk factors for 1-time versus recurrent or multiple fallers. METHODS: The purpose of this research was to examine 2 relationships: (1) the risk factors for nonfallers versus fallers (1+ falls); and (2) the risk factors for nonfallers/1-time fallers versus multiple fallers (2+ falls). All participants (n = 453) were subjects within 5 different fall intervention programs funded through the Falls Prevention Initiative sponsored by Health Canada and Veterans Affairs Canada. In total, 5 project sites funded in Ontario conducted independent fall intervention programs. At the onset of their programs and at the completion of their programs, each project site assessed all of their subjects or a predetermined number of seniors (if the subject pool was extensive) using 2 instruments, namely the interRAI Community Health Assessment and the Berg Balance Scale, so that comparisons could be made between sites. RESULTS: Of the 453 individuals, 67% of the sample was classified as nonfallers, with 33% classified as experiencing 1 or more falls. Risk factors significant within the model examining nonfallers versus 1+ fallers included increased medication use and a previous history of falling. For the second analyses, examining 0 falls/1 fall versus recurrent fallers, the following factors were associated with increased risk: medication use, previous history of falling, and compromised activities of daily living (ADL). Fourteen percent of the sample experienced 2+ falls. CONCLUSIONS: It is important to distinguish fallers based on fall status because recurrent or multiple fallers are more likely to benefit from fall prevention efforts. Using a standardized and comprehensive tool such as the interRAI-CHA would assist researchers in making comparisons between different research groups.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.033
GPT teacher head0.349
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations77
Published2009
Admission routes2
Has abstractyes

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