Abstract P116: Differences In Shared Decision Making Among Patients With Hypertension
Bibliographic record
Abstract
Background: Shared decision-making (SDM) is sanctioned by US legislation and promoted by hypertension guidelines. However, evidence is scarce for ethnically diverse groups. We aimed to examine the relationships between SDM, blood pressure, self-identified race/ethnicity, and baseline characteristics among patients with hypertension. Methods and results: Secondary data analysis of a pragmatic clinical trial with individuals with uncontrolled hypertension using linear regression and generalized estimating equations. Participants (n=1426) were predominantly middle-aged (mean 60 years ± 11.6), female (59.4%, 847), non-Latino Black people (59%, 844), and high school graduates or below (65%, 931). SDM was averaged at 7.2 (±2.6) out of 9, SBP at 152.2 (±12.0), and DBP at 85.3 (±12.1) mmHg. We found a greater reduction in SBP over time with higher SDM mean score (B -0.44, p 0.032). Positive association was found between SDM and patient activation (B 0.01, p=0.001), considering taking blood pressure medication as very important (B 0.56, p=0.022), and patients’ self-reported race/ethnicity. Non-Latino Black people had 0.74 points higher mean SDM score (p<0.001) than Non-Latino White people. On the contrary, the association was negative for knowing that high blood pressure is the same as hypertension (B -0.35, p 0.046), and education. Those with more than high school education had lower mean SDM (B -0.44, p 0.045). Conclusions: Future research should further explore SDM differences among racial/ethnic groups to better align care with needs of patients with hypertension. Our results offer actionable information to enhance clinical practice and policy development.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".