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Record W4378071925 · doi:10.56801/seejph.vi.366

Disentangling the relationship between falls, fear of falling, physical function and walking by applying a socioecological framework to the International Mobility in Aging Study

2023· article· en· W4378071925 on OpenAlexaff
Phoebe W. Hwang, Mohammad Auais, Afshin Vafaei, Nicole Rosendaal, Yan Yan Wu, Catherine M. Pirkle

Bibliographic record

VenueSouth Eastern European Journal of Public Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsLakehead UniversityQueen's University
Fundersnot available
KeywordsFear of fallingFalling (accident)PsychologyInjury preventionGerontologyPhysical activityPoison controlPhysical medicine and rehabilitationMedicineEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Introduction:The relationships between falls, fear of falling, poor mobility, and PA avoidance occur in a cyclic, multi-directional fashion. Aim: This study investigates the concomitant associations of fall history, fear of falling, and physical performance (SPPB) on physical activity using a cross-national sample of community-dwelling older adults from middle and high-income countries.Methods:Linear mixed-effects models looking at the influence of individual and environmental factors were used and participants were nested within each study site.Results:Estimated walking minutes was 52% lower for those with low SPPB compared to high SPPB, 20% lower for those with medium level fear of falling compared to low levels, and 50% lower for those with high level fear of falling compared to low levels.Conclusion:An individual’s fear of falling and physical performance may be important to consider when making PA recommendations to older adults regardless of sex, age, and environment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.058
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.149
GPT teacher head0.389
Teacher spread0.240 · 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 teacher head, 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

Citations0
Published2023
Admission routes1
Has abstractyes

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