What guidance exists to support patient partner compensation practices? A scoping review of available policies and guidelines
Bibliographic record
Abstract
BACKGROUND: An integral aspect of patient engagement in research, also known as patient and public involvement, is appropriately recognising patient partners for their contributions through compensation (e.g., coauthorship, honoraria). Despite known benefits to compensating patient partners, our previous work suggested compensation is rarely reported and researchers perceive a lack of guidance on this issue. To address this gap, we identified and summarised available guidance and policy documents for patient partner compensation. METHODS: We conducted this scoping review in accordance with methods suggested by the JBI. We searched the grey literature (Google, Google Scholar) in March 2022 and Overton (an international database of policy documents) in April 2022. We included articles, guidance or policy documents regarding the compensation of patient partners for their research contributions. Two reviewers independently extracted and synthesised document characteristics and recommendations. RESULTS: We identified 65 guidance or policy documents. Most documents were published in Canada (57%, n = 37) or the United Kingdom (26%, n = 17). The most common recommended methods of nonfinancial compensation were offering training opportunities to patient partners (40%, n = 26) and facilitating patient partner attendance at conferences (38%, n = 25). The majority of guidance documents (95%) suggested financially compensating (i.e., offering something of monetary value) patient partners for their research contributions. Across guidance documents, the recommended monetary value of financial compensation was relatively consistent and associated with the role played by patient partners and/or specific engagement activities. For instance, the median monetary value for obtaining patient partner feedback (i.e., consultation) was $19/h (USD) (range of $12-$50/h). We identified several documents that guide the compensation of specific populations, including youth and Indigenous peoples. CONCLUSION: Multiple publicly available resources exist to guide researchers, patient partners and institutions in developing tailored patient partner compensation strategies. Our findings challenge the perception that a lack of guidance hinders patient partner financial compensation. Future efforts should prioritise the effective implementation of these compensation strategies to ensure that patient partners are appropriately recognised. PATIENT OR PUBLIC CONTRIBUTIONS: The patient partner coauthor informed protocol development, identified data items, and interpreted findings.
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 | 0.161 | 0.445 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.056 | 0.042 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".