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Record W4408788311 · doi:10.1177/07334648251328427

Personality Traits and Health Behaviors as Predictors of Fall Among Community-Dwelling Older Adults: Findings From the Canadian Longitudinal Study on Aging

2025· article· en· W4408788311 on OpenAlexafffundabout
Henrietha C. Adandom, Chiedozie James Alumona, Israel I. Adandom, Adesola C. Odole, Lisa L. Cook, Gongbing Shan, Olu Awosoga

Bibliographic record

VenueJournal of Applied Gerontology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsAlberta Health ServicesUniversity of Lethbridge
FundersCanadian Institutes of Health Research
KeywordsConscientiousnessBig Five personality traitsLongitudinal studyOpenness to experienceLogistic regressionPersonalityGerontologyDemographyMedicineAlcohol consumptionPoison controlInjury preventionPsychologyClinical psychologyExtraversion and introversionEnvironmental healthInternal medicineAlcohol

Abstract

fetched live from OpenAlex

Objectives: To examine whether personality traits and health behaviors predict falls in community-dwelling older adults. Methods: Longitudinal data from the Canadian Longitudinal Study on Aging (CLSA) at baseline (2011–2015) and follow-up two (2018–2021) were analyzed using logistic regression for 5270 adults aged 65 and older, with an alpha level of 0.05. Results: At baseline, participants’ mean age was 72 years, with 51.1% female. Most identified as White (96.7%) and had education beyond secondary (81.5%). Increased physical activity (OR: 1.012, 95% CI: 1.01–1.014), decreased alcohol consumption (OR: 1.634, 95% CI: 1.419–1.883), and smoking cessation (OR: 2.8, 95% CI: 2.198–3.568) increased fall risk, while conscientiousness (OR: 0.832, 95% CI: 0.792–0.874) and openness (OR: 0.959, 95% CI: 0.922–0.998) were protective at follow-up two. Personality changes significantly influence falls. Discussion: Findings highlight the complex interplay between personality traits, health behaviors, and falls, suggesting a one-size-fits-all approach to fall prevention may be insufficient.

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)0.003
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 score0.483
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.063
GPT teacher head0.390
Teacher spread0.327 · 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

Citations4
Published2025
Admission routes3
Has abstractyes

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