Abstract 20525: The Cumulative Burden of Potentially Modifiable Cardiac Risk Factors is Modestly Associated With a Perceived Need to Improve Physical Health: A Population-Based Study of Canadian Adults
Bibliographic record
Abstract
Introduction: An individual’s perceived need to improve physical health (PNIPH) is an essential precursor to adopting healthy behaviors. Nine potentially modifiable risk factors (PMRFs) for myocardial infarction collectively account for ≥90% of the population attributable risk: smoking, hypertension, diabetes, abdominal obesity, psychosocial factors, low fruit and vegetable consumption, lack of or excessive alcohol intake, physical inactivity, and raised apolipoprotein B/A1 ratio. Though widely recognized, their impact on individuals’ health perceptions is unclear. Methods: Residents from 6 provinces were administered a module on changes to improve health as part of the 2011-2012 Canadian Community Health Survey (CCHS). The CCHS uses a complex survey design with stratification, multiple stages of selection, and unequal probability sampling. Sampling weighting was therefore taken into account to derive estimates that are representative of the covered population and an approximate method for incorporating the survey design effect was used. Modified Poisson regression was used to calculate prevalence ratios (PRs) of individual and collective PMRFs for PNIPH. Relevant data were available for 8 of the 9 PMRFs sought. Results: In total, 45,443 adult respondents were included, representing 11,006,123 Canadians and corresponding to 96.8% of the adult population of the sampled provinces. The sum of PMRFs was positively associated with PNIPH (adjusted PR 1.08, 95% CI 1.07-1.09 for each additional PMRF) with 82.3% of individuals with ≥5 PMRFs reporting this perception. After adjusting for potential confounders, the PMRFs most strongly associated with PNIPH were obesity (PR 1.16, 95% CI 1.12-1.21), smoking (PR 1.15, 95% CI 1.11-1.19), and physical inactivity (PR 1.13, 95% CI 1.10-1.16). Diabetes and a lack of or excessive alcohol intake were not associated with PNIPH. Conclusions: PMRFs are differentially associated with PNIPH yet their cumulative burden is positively associated with this perception. Among those at highest cardiac risk, approximately 1 in 5 denied PNIPH. A greater understanding of factors underlying health perceptions and behaviors is needed to capitalize on cardiovascular preventive healthcare efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".