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Record W1976936092 · doi:10.1177/0898264309340694

An Exploratory Study of Individual and Environmental Correlates of Fear of Falling Among Community-Dwelling Seniors

2009· article· en· W1976936092 on OpenAlexaff
Johanne Filiatrault, Johanne Desrosiers, Lise Trottier

Bibliographic record

VenueJournal of Aging and Health · 2009
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de SherbrookeUniversité de Montréal
Fundersnot available
KeywordsFear of fallingFalling (accident)GerontologyExploratory researchPsychologyPoison controlSuicide preventionEnvironmental healthMedicinePsychiatrySociology

Abstract

fetched live from OpenAlex

Objectives. The objective of this study was to identify individual and environmental correlates of fear of falling among community-dwelling seniors. Method. The study sample involved 288 community-dwelling adults aged 65 years or older going through the normal aging process. Fear of falling and a series of individual and environmental characteristics were measured with a questionnaire during home interviews. Results. Multivariate logistic regression procedures showed that the strongest correlates of fear of falling are gender, support from a spouse or partner, and residential area. Being a female as well as living in a smaller city or rural area were shown to be risk factors for fear of falling, whereas the availability of support from a spouse or partner was a protective factor. Discussion. Findings from this study suggest that researchers should adopt an ecological perspective to understanding the phenomenon of fear of falling among seniors and collect data on a broader range of individual and environmental factors.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.364
Teacher spread0.317 · 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

Citations31
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Aging and HealthSame topicBalance, Gait, and Falls PreventionFrench-language works237,207