Hypertension management research priorities from patients, caregivers, and healthcare providers: A report from the Hypertension Canada Priority Setting Partnership Group
Bibliographic record
Abstract
Patient- and stakeholder-oriented research is vital to improving the relevance of research. The authors aimed to identify the 10 most important research priorities of patients, caregivers, and healthcare providers (family physicians, nurses, nurse practitioners, pharmacists, and dietitians) for hypertension management. Using the James Lind Alliance approach, a national web-based survey asked patients, caregivers, and care providers to submit their unanswered questions on hypertension management. Questions already answered from randomized controlled trial evidence were removed. A priority setting process of patient, caregiver, and healthcare providers then ranked the final top 10 research priorities in an in-person meeting. There were 386 respondents who submitted 598 questions after exclusions. Of the respondents, 78% were patients or caregivers, 29% lived in rural areas, 78% were aged 50 to 80 years, and 75% were women. The 598 questions were distilled to 42 unique questions and from this list, the top 10 research questions prioritized included determining the combinations of healthy lifestyle modifications to reduce the need for antihypertensive medications, stress management interventions, evaluating treatment strategies based on out-of-office blood pressure compared with conventional (office) blood pressure, education tools and technologies to improve patient motivation and health behavior change, management strategies for ethnic groups, evaluating natural and alternative treatments, and the optimal role of different healthcare providers and caregivers in supporting patients with hypertension. These priorities can be used to guide clinicians, researchers, and funding bodies on areas that are a high priority for hypertension management research for patients, caregivers, and healthcare providers. This also highlights priority areas for improved knowledge translation and delivering patient-centered care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".