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Record W2921809488 · doi:10.1111/opn.12230

Fear of falling among Brazilian and Portuguese older adults

2019· article· en· W2921809488 on OpenAlexaff
Luciano Magalhães Vitorino, Cristina María Alves Marques-Vieira, Gail Low, Luís Sousa, Jonas Preposi Cruz

Bibliographic record

VenueInternational Journal of Older People Nursing · 2019
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFear of fallingPortugueseMedicineGerontologyFalling (accident)Cross-sectional studyOlder peopleInjury preventionPoison controlDemographyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Falling is the leading cause of physical disability, mortality and social exclusion in older adults. In Brazil and Portugal, falls cause thousands of hospitalisations every year. Fear of falling (FOF) causes loss of confidence in accomplishing daily tasks, restriction in social activities and increased dependence. AIM: To compare the prevalence of FOF between Brazilian and Portuguese community-dwelling older adults and the factors associated with FOF. METHODS: A secondary analysis of cross-sectional survey data collected from older adults residing in Brazil (n = 170; M age=70.44 years) and Portugal (n = 170; M age=73.56 years). RESULTS: The prevalence of FOF was significantly higher (p = 0.015) among Portuguese (n = 133, 54.1%) versus Brazilian (n = 113, 45.9%) older adults. FOF among Brazilian older adults was associated with being 76 + years of age and female. Among Portuguese older adults, factors associated with FOF were intake of daily medications, having fallen within the past year, and visual difficulties. CONCLUSIONS: Fear of falling is linked with modifiable and non-modifiable factors. Timely assessments of FOF and factors associated with FOF are essential. IMPLICATIONS FOR PRACTICE: Primary care nurses should assess and address FOF in older people with interdisciplinary practitioners.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.076
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.342
Teacher spread0.331 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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