Recognizing patient partner contributions to health research: a systematic review of reported practices
Bibliographic record
Abstract
BACKGROUND: Patient engagement in research refers to collaboration between researchers and patients (i.e., individuals with lived experience including informal caregivers) in developing or conducting research. Offering non-financial (e.g., co-authorship, gift) or financial (e.g., honoraria, salary) compensation to patient partners can demonstrate appreciation for patient partner time and effort. However, little is known about how patient partners are currently compensated for their engagement in research. We sought to assess the prevalence of reporting patient partner compensation, specific compensation practices (non-financial and financial) reported, and identify benefits, challenges, barriers and enablers to offering financial compensation. METHODS: We conducted a systematic review of studies citing the Guidance for Reporting the Involvement of Patients and the Public (GRIPP I and II) reporting checklists (October 2021) within Web of Science and Scopus. Studies that engaged patients as research partners were eligible. Two independent reviewers screened full texts and extracted data from included studies using a standardized data abstraction form. Data pertaining to compensation methods (financial and non-financial) and reported barriers and enablers to financially compensating patient partners were extracted. No formal quality assessment was conducted since the aim of the review is to describe the scope of patient partner compensation. Quantitative data were presented descriptively, and qualitative data were thematically analysed. RESULTS: The search identified 843 studies of which 316 studies were eligible. Of the 316 studies, 91% (n = 288) reported offering a type of compensation to patient partners. The most common method of non-financial compensation reported was informal acknowledgement on research outputs (65%, n = 206) and co-authorship (49%, n = 156). Seventy-nine studies (25%) reported offering financial compensation (i.e., honoraria, salary), 32 (10%) reported offering no financial compensation, and 205 (65%) studies did not report on financial compensation. Two key barriers were lack of funding to support compensation and absence of institutional policy or guidance. Two frequently reported enablers were considering financial compensation when developing the project budget and adequate project funding. CONCLUSIONS: In a cohort of published studies reporting patient engagement in research, most offered non-financial methods of compensation to patient partners. Researchers may need guidance and support to overcome barriers to offering financial compensation.
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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 | 0.078 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.028 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".