Building Capacity for Patient-Oriented Research: Utilizing Decision Aids to Translate Evidence into Practice, Policy and Outcomes
Bibliographic record
Abstract
Background: The aim of this project was to engage with patient partners to translate knowledge about the decision aids and develop a scaling-up strategy for wider effects and reach.Method: This project was guided by the World Health Organization and IDEAS (Integrate, Design, Assess and Share) frameworks for design thinking (e.g., ideating creative strategies), dissemination (e.g., sharing locally and widely) and scalability.Results: We engaged 132 stakeholders in six webinars, had 321 total page views of the decision aids and conducted 16 interviews to determine revisions to the design of the decision aids before scalability.Conclusion: Patient-partner collaborations assisted with design thinking, dissemination and scalability. Key Points• Commitment to research projects can be difficult.Patient partners need to feel safe enough to disclose the challenges they face, and research team members need to be respectful and responsive to the needs of the patient partner.• Key stakeholders have collaborated to co-design innovative web-based open-access patient and investigator decision aids to support patient-oriented research (POR).• Funding agencies should consider making POR training mandatory for all investigators and patient partners (e.g., decision aid completion) before making POR funding decisions.P = Patient partner.
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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 | 0.605 | 0.546 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.044 | 0.037 |
| Open science | 0.007 | 0.038 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".