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Record W3067864548 · doi:10.1123/japa.2019-0485

Exploring Fear of Falling and Exercise Self-Efficacy in Older Women With Vertebral Fractures

2020· article· en· W3067864548 on OpenAlexafffund
Matteo Ponzano, Jenna C. Gibbs, Jonathan D. Adachi, Maureen C. Ashe, Angela M. Cheung, Keith Hill, David L. Kendler, Aliya Khan, Caitlin McArthur, Αλεξάνδρα Παπαϊωάννου, Lehana Thabane, John D. Wark, Lora Giangregorio

Bibliographic record

VenueJournal of Aging and Physical Activity · 2020
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of British ColumbiaMcGill UniversityUniversity Health NetworkUniversity of TorontoMcMaster UniversityUniversity of Waterloo
FundersCanadian Institutes of Health Research
KeywordsFear of fallingFalling (accident)MedicinePhysical therapyPhysical medicine and rehabilitationPhysical activityInjury preventionPoison controlQuality of life (healthcare)PsychologyPsychiatryMedical emergency

Abstract

fetched live from OpenAlex

Fear of falling is a common issue among older adults, which decreases quality of life and leads to an avoidance of activities they are still able to do. The goal of this secondary data analysis was to explore the relationship between fear of falling and exercise self-efficacy in 141 women with at least one nontraumatic Genant Grade 2 vertebral fracture. Fear of falling, exercise self-efficacy, history of falling, the number of falls, the use of assisting devices, and pain at rest or during movement were obtained using medical history and health status questionnaires. There was a negative association between fear of falling and exercise self-efficacy (pseudo R2 = .253; p = .004), which persisted when the analysis was adjusted for history and number of falls, use of assistive devices, and pain at rest (pseudo R2 = .329; p < .0001) or during movement (pseudo R2 = .321; p < .0001). Fear of falling may be negatively associated with exercise self-efficacy in older women with vertebral fracture.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.046
GPT teacher head0.335
Teacher spread0.290 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

Explore more

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