Housing Relocation and Residential Satisfaction After Relocation: Effects of Dwelling Condition Changes on Older Adults in the Community
Bibliographic record
Abstract
BACKGROUND: This study seeks to analyze the trajectories of residential satisfaction among older adults before and after relocation and explore the variability in the relationship between relocation and residential satisfaction based on changes in housing conditions during the relocation process. METHODS: Utilizing a nationally representative longitudinal dataset of older adults (N = 2718), this study employs individual-level fixed effect regression models to estimate the association between the timing of relocation and residential satisfaction. Stratified analyses are also conducted to explore how this association varies based on changes in housing conditions. RESULTS: Residential satisfaction tends to decrease before relocation, peaks at the time of relocation, and maintain higher levels as older adults adapt to their new environment. This adaptation process varies depending on changes in dwelling conditions during relocation, with transitions from poor to non-poor housing conditions positively affecting psychological responses, while moves from non-poor to poor conditions can lead to increased psychological burden and prolonged adjustment periods. DISCUSSION: Aging policies can prioritize programs that facilitate adjustment to new environments to improve residential satisfaction of older adults, thereby promoting healthy 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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".