Self-management and blood pressure control in China: a community-based multicentre cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: This study explored the relationship between self-management and blood pressure (BP) control in China. DESIGN: A cross-sectional study. SETTING: Eight community health centres from four cities in the Northeast (Shenyang), Northwest (Xi'an), Southwest (Chengdu) and South (Changsha) of China. PARTICIPANTS: A total of 873 adults with hypertension, including 360 men and 513 women. Hypertension was defined as systolic BP ≥140 mm Hg and/or diastolic BP ≥90 mm Hg. OUTCOME MEASUREMENTS: BP control was the primary outcome variable. This was categorised as good control if individuals with hypertension reduced their BP to <140/90 mm Hg, otherwise, it was categorised as poor control. Secondary outcomes included self-management, defined as: (1) context or condition-specific factors or physical/social environments (eg, age, sex, marital status, education, personal income and health insurance) and (2) process or knowledge/beliefs, self-regulation skills/abilities and social facilitation (eg, treatment, diet, exercise and risk factor management). Data were analysed using logistic regression models using SPSS V.20. RESULTS: A total of 67.1% (n=586) participants had poor BP control. Limited outpatient care benefits in mainly rural residents (OR 2.26, 95% CI 1.06 to 4.81) and longer disease duration (OR 1.03, 95% CI 1.01 to 1.04) were associated with poor BP control. Self-management practices reduced the odds of having poor BP control (OR 0.98, 95% CI 0.97 to 0.99). CONCLUSIONS: The individual and family self-management theory can serve as an effective theory for understanding the key contexts, processes and outcomes essential for BP control in China. Future research should evaluate the effect of a self-management intervention (eg, self-monitoring, medication adherence, regular and routine doctor visits, and social supports) for BP control in China using a multisite cluster randomised controlled trial. Sex and gender difference, cost and patient-reported outcomes should also be examined.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".