Proximate and Contextual Socioeconomic Determinants of Mortality: Multilevel Approaches in a Setting with Universal Health Care Coverage
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
Investigations of contextual factors (income inequality, cultural disruption, access to health and social services, safety and crime rate, and others) have received little emphasis by epidemiologists, although a few have demonstrated the importance of such factors for mortality, particularly in the United States. To expand current understanding of the importance of contextual factors, the authors evaluated mortality in a longitudinal study in Nova Scotia, Canada, where all residents have greater access to health and social services and where income inequalities are smaller than in the United States. A total of 2,116 participants were followed from 1990 through December 1999, linked to the 1991 Canada Census as a source of neighborhood characteristics, and analyzed using individual-level and multilevel logistic regression. Well-educated and high-earning persons fared better. Neighborhood socioeconomic characteristics (neighborhood income, educational level, unemployment rate), in contrast, were not significantly associated with mortality. However, within advantaged neighborhoods, the importance of individual income and education for mortality was increased relative to disadvantaged neighborhoods. The latter findings may direct health policy aimed at reducing health inequalities.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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