What are the decisions and decisional needs of patients with brain-heart conditions, and their caregivers? A survey study
Bibliographic record
Abstract
Abstract Purpose People with brain (e.g. stroke, depression) and heart (e.g., heart failure, irregular heart rhythms) conditions often have both together. Little is known about whether people know about this connection, how it impacts health decision-making and their decisional needs. This study sought to identify common decisions and decisional needs of people living with brain-heart conditions or their caregivers. Methods Using a co-production approach, we designed a survey with a team of researchers, clinicians, patients and caregivers. Eligible participants were adults living with a brain-heart condition, or family members/caregivers, who have made a health decision for themselves or their loved one about their condition within the past 12 months. Potential participants were identified using electronic health records from outpatient clinics and advertisements through community health centres, community social media groups, and newsletters of brain-heart and caregiver related organizations. We cross-sectionally assessed and descriptively analyzed decisions and decisional needs using questions informed by the Ottawa Decision Support Framework, including the validated Decision Conflict Scale (DCS) and the Decision Regret Scale (DRS). Results We recruited 48/50 (96%) patients/caregivers from June 2024 to January 2025. Twenty-six (54%) had a combined brain-heart condition and 22(46%) had a heart condition and at risk for a brain condition. Common decisions were about lifestyle changes 14(32%), medications 11(25%), procedures 10(23%), and diagnostic tests 5(11%). Factors making decisions difficult were that brain implications were never part of the conversation for their heart condition (36%), worrying about choosing the wrong option (32%), difficulty discussing the decision with important others (23%) and feeling overloaded with information (23%). Nine participants (22%) had significant decisional conflict (>37.5/100) and 6 participants (17%) had significant decision regret (>25/100). If they had to make the same decision again, 24 (59%) would want to discuss options and their advantages/disadvantages with their clinicians, 18 (44%) would want reliable information about the options, 17 (41%) would desire access to a patient decision aid, and 17 (41%) would want to speak with someone who has made the same decision recently. Conclusion Patients/caregivers in our study reported unmet decisional needs regarding brain-heart health, most notably limited discussion about the brain-heart link. Interviews with a subset of survey respondents are ongoing, and will offer a deeper understanding into their responses. Interventions can be designed to support patients facing brain-heart health-related decisions by targeting their decisional 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".