Associations of social cohesion and quality of life with objective and perceived built environments: a latent profile analysis among seniors
Bibliographic record
Abstract
BACKGROUND: Healthy aging requires support from local built and social environments. Using latent profile analysis, this study captured the multidimensionality of the built environment and examined relations between objective and perceived built environment profiles, neighborhood social cohesion and quality of life among seniors. METHODS: In total, 693 participants aged 66-97 were sampled from two US locales in 2005-2008 as part of the Senior Neighborhood Quality of Life Study (SNQLS). Perceived social cohesion and quality of life were assessed using validated surveys. Six objective (geographic information system (GIS)-based) and seven perceived built environment latent profiles generated in previous SNQLS publications were used for analyses. Mixed-effects models estimated social cohesion and quality of life separately as a function of the built environment profiles. RESULTS: More walkable and destination-rich perceived built environment profiles were associated with higher social cohesion and quality of life. Objective built environment profiles were not associated with social cohesion and only positively associated with quality of life in only one locale (Baltimore/DC). CONCLUSIONS: Latent profile analysis offered a comprehensive approach to assessing the built environment. Seniors who perceived their neighborhoods to be highly walkable and recreationally dense experienced higher neighborhood social cohesion and quality of life, which may set the stage for healthier aging.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".