Influence of the Social Environment on Ideal Cardiovascular Health
Bibliographic record
Abstract
Background The environment plays a large role in the health of individuals; however, more research is needed to better understand aspects of the environment that most influence health. Specifically, our study examines how the social environment influences cardiovascular health (CVH). Methods and Results The social environment was characterized using measures of belonging and life and work stress in individuals, as well as nationally derived measures of marginalization, deprivation, economic status, and community well-being in neighborhoods. CVH was defined by the American Heart Association's Cardiovascular Health Index-a summed score of 7 clinical and behavioral components known to have the greatest impact on CVH. Data were obtained from the Canadian Community Health Survey 2015 to 2016 and multiple national data sources. Multilevel regression models were used to analyze the associations between CVH and the social environment. Overall, 27% of Canadians reported ideal CVH (6-7 score points), 68% reported intermediate CVH (3-5 score points), and 5% reported poor CVH (0-2 score points). The neighborhood environment contributed up to 7% of the differences in CVH between individuals. Findings indicated that residing in a neighborhood with greater community well-being (odds ratio [OR], 1.33 [95% CI, 1.26-1.41]) was associated with achieving higher odds of ideal CVH, while weaker community belonging (OR, 0.67 [95% CI, 0.62-0.72]) and residing in a neighborhood with greater marginalization (OR, 0.87 [95% CI, 0.82-0.91]) and deprivation (OR, 0.67 [95% CI, 0.64-0.69]) were associated with achieving lower odds of ideal CVH. Conclusions Aspects of individual-level social environment and residing in a neighborhood with a more favorable social environment were both independently and significantly associated with achieving ideal CVH.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".