Identifying potential barriers and solutions to patient partner compensation (payment) in research
Bibliographic record
Abstract
Research that engages patients on the research team is often supported by grant funding from different organizations and, in some cases, principal investigators (who control the grant funding) provide patient partners with compensation (or payment) for their contributions. However, we have noted a gap in resources that identify and address barriers to compensating patient partners (no matter the size, degree or length of their engagement). In this paper, we present thoughts and experiences related to barriers to compensating patient partners with the goal of helping individuals identify and find solutions to these obstacles. Based on our experiences as individuals who live with chronic conditions and are patient partners, and those who are researchers who engage patient partners, we have identified eight barriers to compensating patient partners. We discuss each of these barriers: lack of awareness about patient partnership, institutional inflexibility, policy guidance from funders, compensation not prioritized in research budgets, leadership hesitancy to create a new system, culture of research teams, preconceived beliefs about the skills and abilities of patient partners, and expectations placed on patient partners. We demonstrate these barriers with real life examples and we offer some solutions. To further demonstrate these barriers, we ask readers to reflect on some scenarios that present realistic parallel situations to those that patient partners face. The intention is to illustrate, through empathy or putting yourself in someone else's shoes, how we might all do better with respect to institutional barriers related to patient partner compensation. Last, we issue a call to action to share resources and identify actions to overcome these barriers from which we will create an online resource repository.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.282 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.027 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.006 | 0.034 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".