Multilevel Factors for Life Satisfaction Among Residents in Non-Urban Subsidized Senior Housing
Bibliographic record
Abstract
Abstract Although a quarter of HUD-assisted properties for older adults are located in rural/non-metropolitan areas, there is limited understanding of the population living in these locations. Advanced age and low income are known risk factors for poor physical and mental health. Older adults in rural subsidized housings may be at increased risk for poor health and social isolation due to their isolated locations and small-scale housing complexes. This presents the additional challenge of service provision for the residents’ needs. This study aims to explore multi-level factors affecting life satisfaction among residents in subsidized senior housing. Data were collected for five subsidized senior housings in New Hampshire: two in Coos county (rural, population=33,055) and three in Strafford county (suburban, population=128,613). Mixed-methods approaches were used: Community/organizational-level data were collected using semi-structured interviews conducted with the directors of senior housings. At the individual level, quantitative survey data were collected from 82 residents of five senior housings. Contrary to expectations, we found that residents in rural senior housings were likely to report better life satisfaction (Coef.=0.597, p<.01) than those in suburban areas despite controlling for individual-level factors such as age, gender, education, marital status, health, social relations, and service use. The most salient terms used in the interviews with directors of rural senior housings include limited resources, tight community, and emotional support. The last two may be protective factors positively influencing life satisfaction among their residents. Our results contribute to development strategies to improve quality of life among residents in rural/non-metropolitan subsidized senior housing.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".