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Record W4403465275 · doi:10.47363/jmcn/2023(4)183

Investigation of Frailty, Dependence, Fall Risk Levels, and Influencing Factors in Elderly Individuals

2023· article· en· W4403465275 on OpenAlexaboutno aff
Betül KUŞ

Bibliographic record

VenueJournal of Medical & Clinical Nursing · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyPsychologyDemographyEnvironmental healthMedicineSociology

Abstract

fetched live from OpenAlex

The fragility, dependency and fall risk levels of the elderly are quite high. Physiological changes brought about by aging may cause recurrent falls in elderly individuals, and their quality of life may be negatively affected due to falls. This study was planned to examine fragility, dependence, fall risk, levels of protective behavior against falling, and the affecting factors in the elderly. The cross-sectional, descriptive, and correlational study was carried out with 200 elderly individuals who were admitted to the osteoporosis outpatient clinic of a university hospital. Data collection was done through Elderly Individual Information Form developed by the researchers, the Edmonton Frail Scale, the Barthel Index, the Itaki Fall Risk Scale, and the Fall Behavioral Scale for Older People. The mean age of the elderly who participated in the study was 72.07±6.76 years, 79.5% of the participants were female, 68% were primary school graduates, and 93.0% were retirement pensioners. The mean score of Edmonton Frail Scale was 6.06±2.92, Barthel Index was 94.85±12.57, Itaki Fall Risk Scale was 9.85±0.31, and Fall Behavioral Scale for Older People was 2.89±0.46. Many socio-demographic and fall-related risk factors specific to elderly individuals had an effect on the related scale scores (p < 0.05). The elderly individuals were moderately frail, slightly dependent on others in performing their life activities, had a high risk of fall, and had moderate protective behavior against falls.

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)0.026
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.266
GPT teacher head0.562
Teacher spread0.296 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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