Impact of Frailty on Health Service Used: A Sample of Aksaray
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".