Patients, clinicians and researchers working together to improve cardiovascular health: a qualitative study of barriers and priorities for patient-oriented research
Bibliographic record
Abstract
OBJECTIVES: The overall goal of this study is to identify priorities for cardiovascular (CV) health research that are important to patients and clinician-researchers. We brought together a group of CV patients and clinician-researchers new to patient-oriented research (POR), to build a multidisciplinary POR team and form an advisory committee for the Libin Cardiovascular Institute of Alberta. DESIGN: This qualitative POR used a participatory health research paradigm to work with participants in eliciting their priorities. Therefore, participants were involved in priority setting, and analysis of findings. Participants also developed a plan for continued engagement to support POR in CV health research. SETTING: Libin Cardiovascular Institute of Alberta, Cumming School of Medicine, University of Calgary, Canada. PARTICIPANTS: A total of 23 participants, including patients and family caregivers (n=12) and clinician-researchers (n=11). RESULTS: Participants identified barriers and facilitators to POR in CV health (lack of awareness of POR and poor understanding on the role of patients) and 10 research priorities for improving CV health. The CV health research priorities include: (1) CV disease prediction and prevention, (2) access to CV care, (3) communication with providers, (4) use of eHealth technology, (5) patient experiences in healthcare, (6) patient engagement, (7) transitions and continuity of CV care, (8) integrated CV care, (9) development of structures for patient-to-patient support and (10) research on rare heart diseases. CONCLUSIONS: In this study, research priorities were identified by patients and clinician-researchers working together to improve CV health. Future research programme and projects will be developed to address these priorities. A key output of this study is the creation of the patient advisory council that will provide support and will work with clinician-researchers to improve CV health.
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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 | 0.115 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.027 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".