Patient and public involvement in research: a review of practical resources for young investigators
Bibliographic record
Abstract
Patient and public involvement (PPI) in every aspect of research will add valuable insights from patients' experiences, help to explore barriers and facilitators to their compliance/adherence to assessment and treatment methods, bring meaningful outcomes that could meet their expectations, needs and preferences, reduce health care costs, and improve dissemination of research findings. It is essential to ensure competence of the research team by capacity building with available resources on PPI. This review summarizes practical resources for PPI in various stages of research projects-conception, co-creation, design (including qualitative or mixed methods), execution, implementation, feedback, authorship, acknowledgement and remuneration of patient research partners, and dissemination and communication of research findings with PPI. We have briefly summarized the recommendations and checklists, amongst others, for PPI in rheumatic and musculoskeletal research (e.g. the European Alliance of Associations for Rheumatology (EULAR) recommendations, the Core Outcome Measures in Effectiveness Trials (COMET) checklist and the Guidance for Reporting Involvement of Patients and the Public (GRIPP) checklist). Various tools that could be used to facilitate participation, communication and co-creation of research projects with PPI are highlighted in the review. We shed light on the opportunities and challenges for young investigators involving PPI in their research projects, and have summarized various resources that could be used to enhance PPI in various phases/aspects of research. A summary of web links to various tools and resources for PPI in various stages of research is provided in Additional file 1.
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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 | 0.176 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".