Patients and Families as Partners in Patient-Oriented Research: How Should They Be Compensated?
Bibliographic record
Abstract
Patient and family engagement has become a widely accepted approach in health care research. We recognize that research conducted in partnership with people with relevant lived experience can substantially improve the quality of that research and lead to meaningful outcomes. Despite the benefits of patient-researcher collaboration, research teams sometimes face challenges in answering the questions of how patient and family research partners should be compensated, due to the limited guidance and lack of infrastructure for acknowledging partner contributions. In this paper, we present some of the resources that might help teams to navigate conversations about compensation with their patient and family partners and report how existing resources can be leveraged to compensate patient and family partners fairly and appropriately. We also present some of our first-hand experiences with patient and family compensation and offer suggestions for research leaders, agencies, and organizations so that the health care stakeholders can collectively move toward more equitable recognition of patient and family partners in research.
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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 | 0.175 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".