Social Support, Gender and Central Obesity: A Population-Based Secondary Analysis Study of 28,779 Adults From the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
The well-established link between social isolation and mortality could occur through metabolic effects of poor social support, however research rarely considers different types of social support or effect modification by gender, which we address in this study. Baseline Canadian Longitudinal Study on Aging (CLSA, 2012–2015) Comprehensive cohort data on 28,779 adults (45–85y) provided multiple measures of social support and anthropometric variables. Multiple regression and stratification assessed the association between four support types (informational, emotional, tangible, belonging) and waist circumference (WC) or odds of central obesity (females, WC ≥ 88 cm; males, WC ≥ 102 cm), adjusting for age, age2, education, smoking, and province in all models. Multivariable analyses indicated that average levels of WC were higher among women with lowest, compared to highest, levels of informational (1.89 cm (1.29, 2.49), P < 0.001), belonging (1.54 cm (0.95, 2.14), P < 0.001), emotional (1.47 cm (0.86, 2.07), P < 0.001), or tangible (1.18 cm (0.6, 1.76), P < 0.001) support. Mean WC was only higher among men reporting lowest levels of informational support (+0.74 (0.19, 1.29), P < 0.01), compared to highest. Adjusting for confounders, higher odds of central obesity was associated with a lack of informational, emotional, and tangible support among women (OR range: 1.10–1.23) and men (OR range: 1.10–1.22); central obesity was inversely linked to belonging support in women only (OR 1.20 (1.09, 1.31), P < 0.001). Efforts to lower WC among older Canadians should include access to multiple forms of social support, as different supports may not benefit women and men equally. Our secondary analysis study used the CLSA Baseline Comprehensive Dataset version 4.0, Baseline, under Application Number 19CA003. We acknowledge our Canadian Institute of Health Research grant. We also acknowledge the CLSA cohort study (PIs: Drs. Parminder Raina, Christina Wolfson and Susan Kirkland) that is supported by the Government of Canada through the Canadian Institutes of Health Research under grant reference: LSA 94473 and the Canada Foundation for Innovation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".