Frailty and related factors among community-dwelling older adults in Türkiye
Bibliographic record
Abstract
Abstract Background Frailty in elderly can lead to complex challenges in the follow-up and management of health services. Ensuring comprehensive primary care services and social support can avert, reverse, or mitigate frailty in old age. We aimed to evaluate frailty and its associated factors in individuals aged ≥65 years. Methods We conducted a cross-sectional study in patients applying to primary care centres in 2024, in a city where 10% of the population is elderly. The centres were divided into clusters based on the socioeconomic status of the regions they serve, and nine were randomly selected by weight. A total of 1154 elderly who consecutively presented were included. A questionnaire including sociodemographic characteristics, Edmonton Frailty Scale, Loneliness Scale for the Elderly (LSE), Charlson Comorbidity Index (CCI), International Falls Effectiveness Scale (FES-I) was applied to the elderly. Results The mean age was 71.3±5.3 years, and 50.8% were male. In the study, 20.1% were frail, and 19.6% were apparently vulnerable. It was found that those aged 70-79 and ≥80 years, women, deceased/separated from their spouses, those with low education, those whose occupation was housewife, those earning below minimum wage, those who lived alone, non-exercisers, those taking >8 medications/day, and those who had ≥2 falls/last year were more frail. In multivariate logistic regression analysis, illiteracy (Odds ratio-OR:15.2, 95%CI:1.7-135.5), taking >8 medications/day (OR:4.1, 1.7-9.9), falling ≥2 times/last year (OR:3.4, 1.8-6.4) and ≥4 points on the CCI (OR:1.9, 1.0-3.4) were found to be predictors of frailty. Each score on the FES-I and LSE increased the risk of frailty by 1.08 (1.05-1.11) and 1.13 (1.08-1.17) times, respectively. Conclusions One in five people was found to be frail. Low education level, >8 medications/day, ≥2 falls in the last year, severity of comorbidity, fear of falling, and high levels of loneliness were found to be predictive factors for frailty. Key messages • Encouraging a healthy lifestyle to prevent comorbid diseases and early interventions to avoid falls can reduce frailty. • Providing social activities in primary care and promoting the elderly to participate in these activities can help prevent loneliness, which is a risk factor of 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".