Equality and Equity in Compensating Patient Engagement in Research: A Plea for Exceptionalism
Bibliographic record
Abstract
Engaging citizens and patients in research has become a truism in many fields of health research. It is now seen as a laudable—if not compulsory—activity in research for yielding more impactful and meaningful citizen/patient outcomes and steering research in the right direction. Although this research approach is increasingly common and commendable, we recently encountered a major obstacle in obtaining an ethics certificate from an institutional review board (IRB) to conduct a study that places citizen/patient perspectives on equal footing with those of academic/policy experts. The obstacle was the interpretation of fairness in terms of compensation for research participation (i.e. honoraria). In terms of research ethics, this raised an important question: Should all types of participants be compensated equally, or should exceptions be made for citizen/patient participants? We argue that there are good reasons for exceptionalism and that clearer guidance on citizen/patient engagement in research should be embedded into research ethics doctrine.
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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 | 0.311 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.119 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.006 | 0.036 |
| Research integrity | 0.032 | 0.031 |
| 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; 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".