ASSOCIATIONS BETWEEN MEASURES OF HEALTH AND OBJECTIVE AND SUBJECTIVE NEIGHBORHOOD QUALITY
Bibliographic record
Abstract
Abstract The current study examined the cross-sectional associations between self-reported mental/emotional (e.g., patient health questionnaire), sleep (e.g., Pittsburgh Sleep Quality Index), physical (e.g., medical conditions), and cognitive health (e.g., Montreal Cognitive Assessment) and subjective and objective measures of neighborhood quality. This preliminary analysis included 77 community-dwelling socioeconomically diverse Black and White adults (Mage = 62.17, SDage = 9.71; 70% female) from the HANDLSleep study. Neighborhood quality was assessed using self-reported measures of physical built disorder (e.g., graffiti), social cohesion (e.g., close-knit neighborhood), and social control (e.g., neighbors act if children disrespecting an adult). Area Deprivation Index (ADI) National and State values were extracted from the Neighborhood Atlas using participant addresses. Multivariable linear regression analyses were conducted using health measures as the outcome of interest. Models were adjusted for age, sex, race, education, and poverty status. In the adjusted models, participants living in neighborhoods of greater deprivation (i.e., State ADI) reported higher levels of depressive symptomology (p <.05) and worse sleep quality (p <.05). Likewise, higher reports of physical built disorder were associated with poorer cognition (i.e., attention, p <.05). Contrastingly, reports of better social cohesion were associated with lower insomnia severity scores (p <.05) and better performance on a measure of executive function (p <.01). Reports of more social control was also associated with better executive function (p <.05). These findings highlight the importance of examining both objective and subjective neighborhood characteristics as they pertain to different dimensions of health.
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.
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 | 0.000 |
| 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) | 0.002 | 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".