Advancing Research Alongside Patient Partners: Next-Generation Best Practices for Effective Collaboration in Health Research
Bibliographic record
Abstract
Ovarian Cancer Canada's Patient Partners in Research (PPiR) is a national volunteer-based program that trains and connects individuals with lived ovarian cancer (OC) experience to diverse research opportunities, to maximize the clinical relevance and real-life impact of OC research in Canada. A steadily increasing demand for patient partners to be involved as research team members and decision-makers led us to co-develop with the PPiR team a series of "best practices" for researcher-patient partnerships. This framework formalizes our evolving approach to patient engagement and begins to address challenges that can arise in research settings focused on less commonly diagnosed yet significant and fatal diseases such as OC: (1) Start early. (2) Foster collaboration among the entire research team. (3) Establish expectations and communicate regularly. (4) Report impact of patient partner contributions. (5) Ensure adequate resources. While there are ongoing challenges associated with patient engagement that need to be addressed, data collected from an anonymous survey of Canadian OC researchers show a marked improvement in perceived benefits of patient engagement over time and validate the best practices presented herein. Developed in the context of OC research, these best practices can be adapted to a variety of health research settings with similar challenges.
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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 | 0.361 | 0.293 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.028 | 0.038 |
| Scholarly communication | 0.039 | 0.035 |
| Open science | 0.011 | 0.063 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier 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".