Matching researchers' needs and patients' contributions: practical tips for meaningful patient engagement from the field of rheumatology
Bibliographic record
Abstract
There is an increasing recognition of the importance of patient engagement and involvement in health research, specifically within the field of rheumatology. In general, researchers in this specialty appreciate the value of patients as partners in research. In practice, however, the majority of researchers does not involve patients on their research teams. Many researchers find it difficult to match their needs for patient engagement and the potential contributions from individuals living with rheumatic disease. In this Viewpoint, we provide researchers and patients practical tips for matching 'supply and demand,' based on our own experiences as patient engagement consultants and trainers in rheumatology research. All authors started as a 'naïve' patient or caregiver, an identity that evolved through a process of 'adversarial growth': positive changes that are experienced as a result of the struggle with highly challenging life circumstances. Here, we introduce four stages of adversarial growth in the context of research. We submit that all types of patients have their own experiences, qualities and skills, and can add specific input to research. The recommendations for engagement are not strict directives. They are meant as starting points for discussion or interview. Regardless of individual qualities and knowledge, we believe that all patients engaged in research have a single goal in common: to contribute to research that ultimately will change the lives of many other patients.
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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 | 0.181 | 0.257 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.033 | 0.035 |
| Scholarly communication | 0.035 | 0.046 |
| Open science | 0.008 | 0.065 |
| Research integrity | 0.033 | 0.057 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".