A SYSTEMATIC REVIEW OF GENDER, SOCIAL CONNECTIONS AND RISK OF HYPERTENSION
Bibliographic record
Abstract
Objective: This review aimed to synthesize the longitudinal evidence on the impact of alterations in social connections on the risk of hypertension among aging adults, with a focus on gender. Design and method: A systematic search of four bibliometric databases (OVID/Medline, OVID/Embase, CINAHL, and Scopus) conducted until June 2024. We included prospective studies that assessed change(s) in marital status, living arrangement, social network/contacts, or social participation in relation to change(s) in blood pressure or to incidence of hypertension among adults aged 45 and above. We extracted data using a standardised evidence table and analysed data with narrative synthesis. Results: We found 6645 de-duplicated records and screened titles/abstract for eligibility. We read 29 texts in full and only six studies from three countries met inclusion criteria. Four studies focused on marital transitions and two focused on changes in living arrangement; no longitudinal evidence was found on alterations in other social connections. Most included studies (4/6) had female-only samples. Overall, loss of close social connections had mixed effects on changes in blood pressure or risk of hypertension. More consistent increases in systolic blood pressure or odds of hypertension were seen across studies for aging women who entered marriage. Both increases and decreases in blood pressure or incidence hypertension were seen for women who became or remained unmarried. Studies of living arrangement were male-only or adjusted for sex; remaining lone-living increased the risk of hypertension in older adults. Most studies (n=4) were deemed of medium quality and two were high quality. Nine excluded studies only assessed change in either the exposure or the outcome. Conclusions: Despite ample research on social factors as correlates of cardiovascular outcomes, there is a surprising lack of robust evidence from prospective population-based studies on social connections as determinants of hypertension as a CVD risk factor. Limited research suggests that entering marriage but also becoming or remaining unmarried may result in hypertension among women. Further research is needed to better understand the social determinants of hypertension as actionable evidence to inform social prescribing for prevention of hypertension is still very weak.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".