AGE FRIENDLINESS OF COMMUNITIES CONTRIBUTES TO QUALITY OF LIFE
Bibliographic record
Abstract
Abstract The World Health Organization (WHO) emphasized the importance of age-friendly communities in supporting quality of life for older adults. We aimed to determine the contribution of the age-friendliness of communities to quality of life in a sample of healthy older adults. We used data collected through a longitudinal study on drivers and ex-drivers. We used the World Health Organization Quality of Life instrument (WHOQOL-BREF; WHOQOL Group, 1998) to measure physical health, psychological health, social relationships, and environment. We used the Age-Friendly Survey (AFS; Menec & Nowicki, 2014) to measure 9 domains of participants’ perceptions of community age-friendliness. We estimated 4 multivariable linear regression models. The dependent variables were the 4 domains of the WHOQOL-BREF. Each model had AFS as the focal independent variable and participants’ age, gender, health status, and depression symptoms as control variables. Data from 171 participants were available; mean age was 83.2 years (SD=4.1), 61% were women. Most participants reported a good health status and few depression symptoms. The models explained between 18 and 27% of the variance in WHOQOL scores; community age-friendliness was a statistically significant variable in all models, accounting for 2-3% of the variance. The identification of factors that contribute to quality of life will serve as the foundation upon which policies and interventions to promote successful and healthy aging can be developed. Future work will require consideration of the specific aspects of communities that may affect quality of life the most and that have the most potential for modification.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".