Engaging patient partners to identify research priorities for atrial fibrillation: Results from a patient engagement day
Bibliographic record
Abstract
Objective: We describe a Patient Engagement Day from the Canadian Stroke Prevention Network (C-SPIN). Patients and family members were engaged as patient partners to generate and prioritize future direction for Atrial Fibrillation (AF) research. Methods: A facilitated group discussion methodology was used that included a nominal group brainstorming and decision-making technique designed to foster participation and idea generation. Results: Twenty-four patient partners attended. Priorities related to: 1) need for a curative focus and not new medication (84 %), 2) identification of triggers (53 %), and 3) home-based/remote monitoring (53 %). Use of the Public and Patient Engagement Evaluation Tool (PPEET) found patient partners understood the intent of the day, with its objectives being met. Findings highlighted knowledge gaps by patient partners that were previously thought to be understood. Conclusion: Patient partners could benefit from more focused education about atrial fibrillation. Notably, the priorities identified by patient partners were new to the research team, reinforcing the importance of engaging with the population who will be impacted by the research. Innovation: Little research has been undertaken examining patient partner priorities regarding atrial fibrillation research. This work highlights patient partners' interest in providing input and shaping future research endeavors.
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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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".