Engaging patients and family members to design and implement patient-centered kidney disease research
Bibliographic record
Abstract
We need more research projects that partner and engage with patients and family members as team members. Doing this requires that patients and family members set research priorities and fully participate in research teams. Models for this patient and family member engagement as research partners can help increase patient centered outcomes research. In this article, we describe how we have successfully engaged patients with kidney disease and family members as Co-Investigators on a 5-year research project testing a health system intervention to improve kidney disease care. Background This article describes a method for successful engagement of patients and family members in all stages of a 5-year comparative effectiveness research trial to improve transitions of care for patients from chronic kidney disease to end-stage kidney disease. Methods This project utilized the Patient-Centered Outcomes Research Institute's conceptual model for engagement with patients and family members. We conducted a qualitative analysis of grant planning meetings to determine patient and family member Co-Investigators' priorities for research and to include these engagement efforts in the research design. Patient and family member Co-Investigators partnered in writing this paper. Results Patients and family members were successfully engaged in remote and in-person meetings to contribute actively to research planning and implementation stages. Three patient-centered themes emerged from our data related to engagement that informed our research plan: kidney disease treatment decision-making, care transitions from chronic to end-stage kidney disease, and patient-centered outcomes. Conclusions The model we have employed represents a new paradigm for kidney disease research in the United States, with patients and family members engaged as full research partners. As a result, the study tests an intervention that directly responds to their needs, and it prioritizes the collection of outcomes data most relevant to patient and family member Co-Investigators. Trial registration NCT02722382 .
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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 | 0.316 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".