Multiethnic Perspectives of Shared Decision‐Making in Hypertension: A Mixed‐Methods Study
Bibliographic record
Abstract
Background Shared decision‐making (SDM) has the potential to improve hypertension care quality and equity. However, research lacks diverse representation and evidence about how race and ethnicity affect SDM. Therefore, this study aims to explore SDM in the context of hypertension management. Methods and Results Explanatory sequential mixed‐methods design was used. Quantitative data were sourced at baseline and 12‐month follow up from RICH LIFE (Reducing Inequities in Care of Hypertension: Lifestyle Improvement for Everyone) participants (n=1212) with hypertension. Qualitative data were collected from semistructured individual interviews, at 12‐month follow‐up, with participants (n=36) selected based on their SDM scores and blood pressure outcome. Patients were cross‐ categorized based on high or low SDM scores and systolic blood pressure reduction of ≥10 or <10 mm Hg. Multinomial logistic regression analysis showed that predictors of SDM scores and blood pressure outcome were race and ethnicity (relative risk ratio [RRR], 1.64; P =0.029), age (RRR, 1.03; P =0.002), educational level (RRR, 1.87; P =0.016), patient activation (RRR, 0.98; P <0.001; RRR, 0.99; P =0.039), and hypertension knowledge (RRR, 2.2; P <0.001; and RRR, 1.57; P =0.045). Qualitative and mixed‐methods findings highlight that provider–patient communication and relationship influenced SDM, being emphasized both as facilitators and barriers. Other facilitators were patients' understanding of hypertension; clinicians' interest in the patient, and clinicians' personality and attitudes; and barriers included perceived lack of compassion, relationship hierarchy, and time constraints. Conclusions Participants with different SDM scores and blood pressure outcomes varied in determinants of decision and descriptions of contextual factors influencing SDM. Results provide actionable information, are novel, and expand our understanding of factors influencing SDM in hypertension.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".