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Record W2993682888

Impact of Frailty on Health Service Used: A Sample of Aksaray

2019· article· en· W2993682888 on OpenAlexaboutno aff
Tuğçe Türten Kaymaz, Güler Duru Aşiret

Bibliographic record

VenueDSpace - Düzce (Duzce University) · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsNottingham Health ProfileMedicineScale (ratio)GerontologyQuality of life (healthcare)Sample size determinationNursingStatisticsAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective: Frailty has negative consequences such as reduced quality of life and increased need for specialized care. This study aimed to determine the impact of frailty on health service use among older adults. Methods: A sample of 189 patients (≥ 65 years) was recruited from internal and surgical disease services and polyclinics at the State Hospital in Turkey. Data were collected in face-to-face interviews using an information form, the Edmonton Frail Scale and the Nottingham Health Profile. Relationships between continuous variables were analysed using Pearson rank-correlation coefficient. Multiple linear regression analyses were conducted to determine the association between frailty and each health service use variable. Results: In total, 49.2% of the participants were frail. The mean Nottingham Health Profile score was 163.58 ± 114.03. The Edmonton Frail Scale score increases by 1 unit, the frequency of using health care service increases by 0.892. There were statistically significant moderate positive linear relationships between The Edmonton Frail Scale score, and Nottingham Health Profile score (r=0.692, p<0.001). Conclusions: The frailty frequency was high. The results of this study showed a weak association between frailty and healthcare service application. The quality of life of frail older people is lower.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.296
Teacher spread0.264 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueDSpace - Düzce (Duzce University)Same topicFrailty in Older AdultsFrench-language works237,207