Recognizing patient partner contributions to health research: a mixed methods research protocol
Bibliographic record
Abstract
BACKGROUND: The overall aim of this program of research is to assess when/how patient partners are compensated financially for their contributions to health research. The research program consists of three studies to address the following questions: (1) What is the prevalence of reporting patient partner financial compensation? (2) What are researcher and institutional attitudes around patient partner financial compensation? (3) What are the current practices of patient partner financial compensation and what guidance exists to inform these practices? METHODS: In our first project, we will conduct a systematic review to assess the prevalence of reporting patient partner financial compensation and identify current financial compensation practices on an international scale. We will identify a cohort of published studies that have engaged patients as partners through a forward citation search of the Guidance for Reporting the Involvement of Patients and the Public (GRIPP I and II) checklists. We will extract details of financial compensation (type of financial compensation, amount, payment frequency etc.) and reported benefits, challenges, barriers and enablers to financially compensating patient partners. Quantitative data will be analyzed descriptively, and qualitative data will undergo thematic analysis. In our second project, we will conduct a cross-sectional survey of researchers who have engaged patient partners. We will also survey members of their affiliated institutions to gain further understanding of stakeholder experiences and attitudes with patient partner financial compensation. Survey responses will be analyzed by calculating prevalence. In our third project, we will conduct a scoping review to identify all published guidance and policy documents that guide patient partner financial compensation. Overton, the largest available online database of international policy documents, and the grey literature will be systematically searched. Data items will be extracted and presented descriptively. A comprehensive overview of guidance documents will be presented, which will represent a repository of resources that stakeholders can refer to when developing a financial compensation strategy. DISCUSSION: Our three studies will not only inform and assist patient partners and researchers by informing compensation strategies, but also support the inclusion of diverse perspectives. We will disseminate findings through traditional mediums (publications, conferences) as well as social media, non-technical summaries, and visual abstracts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.219 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one teacher head, 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".