Perceived quality of life and frailty among older people living in different settings
Bibliographic record
Abstract
OBJECTIVE: To analyze the perceived quality of life of older people living in the community and long-term care facilities, and correlate it with the presence of frailty. METHODS: This is a quantitative, analytical, cross-sectional study in which 136 older people were interviewed, half were living in the community and the other half were living in long-term care facilities. The Edmonton Frail Scale was used to identify frailty, and the World Health Organization Quality of Life – Bref (WHOQOL-BREF) and World Health Organization Quality of Life Assessment for Older Persons (WHOQOL-OLD) questionnaires were used to measure quality of life. Analysis of variance and Pearson correlation coefficients were used for intragroup analyses. RESULTS: A greater proportion of older people living in long-term care facilities were frail. Perceived quality of life was better among people living in the community, according to both questionnaires, particularly in the domains social relations, environment, and death and dying. The worst scores were observed in the autonomy domain, particularly among older people living in long-term care facilities. In the majority of domains, older people with frailty had worse perceived quality of life scores. CONCLUSIONS: The absence of frailty favors a better perception of the quality-of-life domains, as does living in the community.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".