Neighborhood characteristics and depressive mood among older adults: an integrative review
Bibliographic record
Abstract
BACKGROUND: There is growing evidence that neighborhood environments are related to depressive mood in the general population. Older adults may be even more vulnerable to neighborhood factors than other adults. The aim of this paper is to review empirical findings on the relationships between neighborhood characteristics and depressive mood among older adults. METHODS: A search of the literature was undertaken in PsycINFO and MEDLINE. RESULTS: Nineteen studies were identified. Study designs were most often cross-sectional, included large sample sizes, and controlled for major individual characteristics. Mediational effects were not investigated. Statistical analysis strategies often included multilevel models. Spatial delimitations of neighborhood of residence were usually based on administrative and statistical spatial boundaries. Six neighborhood characteristics were assessed most often: neighborhood socioeconomic disadvantage, neighborhood poverty, affluence, racial/ethnic composition, residential stability, and elderly concentration. Selected neighborhood characteristics were associated with depressive mood after adjusting for individual variables. These associations were generally theoretically meaningful. CONCLUSIONS: Neighborhood variables seem to make a unique and significant contribution to the understanding of depressive mood among older adults. However, few studies investigated these associations and replication of results is needed. Several substantive neighborhood variables have been ignored or neglected in the literature. The implications of neighborhood effects for knowledge advancement and public health interventions remain unclear. Recommendations for future research are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".