Neighborhood characteristics and depressive mood among older adults: an integrative review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it