Cardiovascular risk in aging adults with double deficits in social support: a gender-sensitive, cross-sectional analysis of the CLSA cohort
Bibliographic record
Abstract
Background: Synergistic effects of different forms of social support (informational, tangible, emotional and belonging) on cardiovascular disease risk factors (CVRF) in aging women versus men is unknown. Aim: To quantify gender differences in the additive and multiplicative associations of four different forms of social support (informational, tangible, emotional and belonging) with cardiovascular disease risk factors (CVRF) in aging adults. Methods: Baseline data from 28,779 adults (45-85 years) in the Canadian Longitudinal Study on Aging Comprehensive cohort (2012-15) with four self-reported social support measures and measured body mass index, waist circumference and blood pressure. We used stratified multivariable principal component regression (PCR) and then post-estimated calculation of adjusted means and 95% CIs. Results: Among women, reporting low availability of two types of social support was consistently associated with the highest adjusted mean levels of WC and BMI. However, lack of two types of support was less clearly or consistently associated with anthropometric measures among men. The highest adjusted mean SBP levels among women were seen for women with low availability of informational support. Among men, low-low combinations of two social supports did not consistently correspond to the highest adjusted mean SBP levels. Results for DBP were null and showed no gender differences. Sensitivity analyses revealed two significant interaction effects of tangible and belonging supports for SBP and WC among women. Conclusion: Findings showed women with two deficits in social supports had the worse CVRF profiles than one social support deficit. Women’s worse CVRF profiles were seen for deficit combinations that included low informational support. We did not, however, find antagonistic/synergistic effects of social support on CVRFs. Heart health care and prevention for aging women would benefit from ensuring informational support with other supports is available.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".