Catalyzing research excellence and impact: Integration of cross cutters in a patient-oriented research study on engaging patients with brain-heart conditions in health decisions
Bibliographic record
Abstract
Abstract Purpose The Brain-Heart Interconnectome (BHI) is a mission-driven interdisciplinary research program seeking to improve the diagnosis, treatment, and prevention of concurrent brain-heart conditions. In the BHI, seven transformative crosscutters drive change to promote research excellence and impact. The crosscutters are 1) Patient Engagement; 2) Research Co-production and Knowledge Mobilization (KMb); 3) Inclusion, Diversity, Equity, Accessibility, and Social Justice (IDEAS); 4) Open Science; 5) Indigenous Engagement; 6) Mentoring and Training; and 7) Commercialization. We aim to describe the integration of these crosscutters, and offer tangible examples of their operationalization, in the design and conduct of a multi-phase patient-oriented research study focused on supporting quality treatment decision-making for people with combined brain-heart conditions and their caregivers. Methods and Results Our study was informed by the needs of people with lived experience of brain-heart conditions, with patients and caregivers as members of the research team to ensure findings will be relevant to users (Patient Engagement). A collaborative approach ensures users of the research and those who will benefit are partners on our diverse research team. This includes patients, caregivers, clinicians, and trainees all of whom are represented on our executive (n=8) and steering committees (n=18). All members have contributed from the proposal writing stage to the ongoing selection of KMb activities, through shared governance and decision-making (Co-production and KMb). Our recruitment strategies target several clinics within two tertiary care hospitals, community health centres, and patient/caregiver facing organizations to ensure a diverse sample. We have embedded the equity-focused PROGRESS-Plus framework in our data collection, and sex- and gender-based analyses are planned for all phases (IDEAS). We abide by open science practices through protocol registration, use of reporting guidelines, development of data management plans and planned pre-prints/open access publications (Open Science). Concurrently, we are building relationships with Indigenous partners to inform future decisional needs assessment and explore tailoring and co-development of decision support interventions (Indigenous Engagement). Along with the crosscutter expertise support and capacity building offered through the BHI, we have created an interdisciplinary training environment in which undergraduate, masters, and doctoral trainees are receiving training and mentorship in rigorous, inclusive, and collaborative brain-heart research. Conclusion We have used several methods and strategies to operationalize these crosscutters in our research conduct and design. This was achieved through careful planning and access to capacity building resources and crosscutter expertise within the BHI, our team’s leadership and commitment to their integration, and adequately budgeted time and resources.
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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 | 0.046 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".