Social integration after moving to a new city predicts lower systolic blood pressure
Bibliographic record
Abstract
Residential mobility is linked to higher incidence of cardiovascular disease (CVD) and mortality. A mechanism by which residential relocation may impact health is through the disruption of social networks. To examine whether moving to a new city is associated with increased CVD risk and whether the extent to which movers rebuild their social network after relocating predicts improved CVD risk and psychosocial well-being, recent movers (n = 26), and age- and sex-matched nonmovers (n = 20) were followed over 3 months. Blood pressure, C-reactive protein/albumin ratio (CRP/ALB), social network size, and psychosocial well-being were measured at intake (within 6 weeks of residential relocation for movers) and 3 months later. Multiple regression indicated higher systolic blood pressure (SBP) for movers (M = 107.42, SD = 11.39), compared with nonmovers (M = 102.37, SD = 10.03) at intake, though this trend was not statistically significant. As predicted, increases in movers' social network size over 3 months predicted decreases in SBP, even after controlling for age, sex, and waist-to-hip ratio, b = -2.04 mmHg, 95% CI [-3.35, -.73]. Associations between increases in movers' social ties and decreases in depressive symptoms and stress were in the predicted direction but did not meet the traditional cutoff for statistical significance. Residential relocation and movers' social network size were not associated with CRP/ALB in this healthy sample. This study provides preliminary evidence for increased SBP among recent movers; furthermore, it suggests that this elevation in CVD risk may decrease as individuals successfully rebuild their social network.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".