Racial and Ethnic Differences in Shared Decision Making Among Patients With Hypertension: Results From the RICH LIFE Project
Bibliographic record
Abstract
Background Racial and ethnic disparities in hypertension care persist. Shared decision making (SDM) is promoted in hypertension guidelines. However, evidence is lacking on how race, ethnicity, and SDM relate, and the effect of SDM on hypertension control in diverse groups. We aimed to explore the relationships among SDM, blood pressure (BP), race and ethnicity, and other decision‐making factors in patients with hypertension. Methods and Results Longitudinal analysis of data from the RICH LIFE (Reducing Inequities in Care of Hypertension: Lifestyle Improvement for Everyone) project's participants (n=1426) with uncontrolled hypertension was performed using descriptive statistics, linear regression, and generalized estimating equations. Participants were middle‐aged (mean age 60±11.6 years), predominantly women (59.4%, 847), non‐Latino Black (59%, 844), and high school graduates or below (65%, 931). The mean SDM score was 7.2±2.6 out of 9, and the mean baseline systolic and diastolic BP were 152.2±12.0 and 85.3±12.1 mm Hg. Non‐Latino Black people had 0.14 points higher mean SDM score ( P <0.001) than non‐Latino White people. Systolic BP reduction over 12 months was greater with a higher SDM mean score (β=−0.42, P =0.035). Baseline characteristics associated with SDM included more than high school education (β=−0.08, P =0.045), hypertension knowledge (β=−0.05, P =0.046), considering taking BP medication as very important (β=0.06, P =0.022), and patient activation (β=0.09, P =0.001). Conclusions There was greater BP reduction for patients with higher SDM score at follow‐up, and associations between SDM and race and ethnicity, education, hypertension knowledge and attitude, and patient activation. Future research should further explore SDM differences among racial and ethnic groups to better align hypertension care with patients' needs.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".