CITY LIVABILITY AND THE WELL-BEING OF OLDER AMERICANS
Bibliographic record
Abstract
Quality of life is a multidimensional construct often conceptualized in terms of social connectedness, happiness, and independence. Because of the common physical and mental declines associated with aging, older persons’ quality of life might be especially sensitive to their social and physical environments. Ecological theories of aging suggest that the well-being of older persons is a function of their competencies and of the challenges and stressors of their environments, yet insufficient attention has been given to macro-level city environments. Buffel and her associates (2012) propose that city environments matter for the happiness of older persons, and these authors have encouraged more comparative research on age-friendly cities. The present study combines two sources of data, individual-level survey data (Wave 1 of the National Social Life, Health, and Aging Project (n=3,005)) and official records about demographic, economic, crime, weather, and arts and leisure characteristics of metropolitan statistical areas in the United States (n=57). Analyses utilize hierarchical linear modelling techniques to study how a diverse set of city-level factors are related to a diverse set of measures of the well-being of older persons. Our results show that measures of income, employment, education, and crime at the city-level are particularly consequential for a diverse set of measures of the well-being of older persons, including mental health, self-esteem, happiness, depression, anxiety, and stress. This macro-level analysis suggests the importance of identifying why these measures of city-level quality of life are particularly influential for the well-being of older persons.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".