Neighbourhood composition and depressive symptoms among older Mexican Americans
Bibliographic record
Abstract
STUDY OBJECTIVE: Research suggests that economically disadvantaged neighbourhoods confer an increased risk of depression to their residents. Little research has been reported about the association between ethnic group concentration and depression. This study investigated the association between neighbourhood poverty and neighbourhood percentage Mexican American and depressive symptoms for older Mexican Americans in the south western United States. DESIGN: A population based study of older non-institutionalised Mexican Americans from the baseline assessment (1993/94) of the Hispanic established population for the epidemiologic study of the elderly (H-EPESE) merged with 1990 census data. SETTING: Five south western states in the United States. PARTICIPANTS: 3050 Mexican Americans aged 65 years or older. MAIN RESULTS: There was a strong correlation between the percentage of neighbourhood residents living in poverty and the percentage who were Mexican American (r = 0.62; p<0.001). Percentage neighbourhood poverty and percentage Mexican American had significant and opposite effects on level of depressive symptoms among older Mexican Americans. After adjusting for demographic and other individual level factors, each 10% increase in neighbourhood population in poverty was associated with a 0.763 (95% CI 0.06 to 1.47) increase in CES-D score, while each 10% increase in Mexican American neighbourhood population was associated with a -0.548 (95% CI -0.96 to -0.13) unit decrease in CES-D score among older Mexican Americans residing in their neighbourhoods. CONCLUSIONS: The findings suggest a sociocultural advantage conferred by high density Mexican American neighbourhoods, and suggest the need to include community level factors along with individual level factors in community based epidemiological health studies.
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 | 0.000 | 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 | 0.000 |
| 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 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".