Examining the Simultaneous Influence of Individual and Neighborhood Determinants on Cardiovascular Health: A Multilevel Study in Canadian Adults
Bibliographic record
Abstract
Introduction: Previous studies have found that the cardiovascular health of individuals may be influenced not only by their personal characteristics, but also independently by their social relationships and the neighborhoods in which they reside. Still, it is unclear how these determinants act together to influence cardiovascular health and whether these determinants account for differences in cardiovascular health among Canadian adults.\nObjective: The main objectives were: 1) to examine the status of cardiovascular health in Canadian adults and, 2) to describe how individual and neighborhood determinants can: a) act together to influence cardiovascular health and, b) account for differences in cardiovascular health among Canadian adults.\nMethods: This study employed a cross-sectional design utilizing secondary data from multiple sources. Cardiovascular health 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 cardiovascular health; ideal health in all 7 components is the healthiest outcome. Data for cardiovascular health was extracted from the Canadian Community Health Survey 2015-2016. Descriptive methods were employed to establish the distribution of cardiovascular health in Canadian adults. Multilevel Mixed Effects Regression Modelling was employed to examine the influence of individual (including interpersonal) and neighborhood determinants on cardiovascular health in a sample of Canadian adults.\nResults: Study findings indicated that 27% of Canadians reported ideal health in 6-7 cardiovascular health components, 68% reported ideal health in 3-5 cardiovascular health components, and 5% reported ideal health in only 0-2 cardiovascular health components. Canadian adults were found to be healthier in clinical, as opposed to behavioral, components of cardiovascular health. Multilevel analyses indicated that individual (including interpersonal) and neighborhood determinants acted simultaneously, and even interactionally, to influence cardiovascular health. Further, the neighborhood accounted for up to 7% of the differences in cardiovascular health between individuals, with considerable differences noted between neighborhoods for the influence of determinants on cardiovascular health.\nConclusion: Interventions to improve cardiovascular health should be aimed at encouraging healthier behaviors in Canadian adults and, addressing both individual and neighborhood determinants of health simultaneously in subgroups with the poorest cardiovascular health.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".