Older people’s awareness of age-related change, life-space mobility, and frailty levels: Türkiye rural example
Bibliographic record
Abstract
Abstract To determine awareness of age-related change, life-space, and frailty levels of older individuals living in rural areas of Türkiye. This research, which was descriptive relationship-seeking type, was conducted between January and April 2024 with 292 individuals aged 65 years and over. Data were collected face to face in public places using Personal Information Form, Awareness of Age-related Change Scale (AARC), Life-Space Assessment (LSA), Edmonton Frail Scale (EFS). Data were evaluated using MANOVA analyses, Pearson correlation analysis, structural equation modeling. The AARC, LSA, and EFS scores varied according to the presence of chronic disease, continuous medication use, and participation in social activities. It was determined that older people’s loss of AARC was positively related to gains (r = 0.394, p < 0.01), positively related to frailty (r = 0.189, p = 0.001), and negatively related to life-space levels (r= - 0.193, p = 0.001). It was also determined that life-space was negatively related to frailty (r= - 0.324, p < 0.001). Model built on older people’s awareness of age-related negative changes, life-space mobility and frailty levels was significant (p < 0.05). Increasing participants’ AARC losses score by one unit reduced LSA by 0.282 units (p = 0.041) and increased EFS scores by 0.180 units (p = 0.031). In model, increasing LSA score by one unit reduced EFS score by 0.755 units (p < 0.001). Increase in negative perception of old age reduces life-space mobility and increases frailty. Additionally, reduced life-space mobility increases frailty.
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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".