Patient and public involvement in research: practical experiences from different settings
Bibliographic record
Abstract
This e-book was strategically designed and organized to address theeducational needs of Patient and Public Involvement (PPI) inResearch, particularly for researchers in countries like Brazil, wherePPI still needs to be widely incorporated into research projects andactivities. It builds upon our first e-book, Patient and Public Involvement inResearch, which introduced the foundational concepts of involvingpatients and the public in research. In this new e-book, we presentpractical and successful experiences of "how to do PPI," contributedby collaborators from various parts of the world. You will find examples of patient involvement across different healthconditions and public engagement from various age groups inresearch led by countries such as England, Canada, Ireland, and theNetherlands. Additionally, initiatives from low- and middle-incomecountries, including Brazil, Chile, Uganda, and Indonesia, are shared. The growing interest in PPI training highlights the increasingrecognition of the value of collaboration between researchers andthe community. The need for PPI implementation is urgent—not onlyto meet this demand but also to transform the research landscape. Effective integration of PPI potentially will redefine how studies areconducted, ensuring that outcomes are more relevant, impactful,and aligned with the real needs and experiences of patients and thepublic involved.The chapters in this e-book were written bymembers of the public, researchers, clinicians, and students atundergraduate, master’s, and doctoral levels, who share theirexperiences with PPI. We hope this publication inspires readers to delve deeper, initiate,and/or refine the application of PPI in all research projects. Since most collaborators are native English speakers, this e-bookwill be published first in English and subsequently translated intoPortuguese and Spanish to disseminate this content further in Braziland Spanish-speaking countries. Follow us for updates (@epp.brasil)
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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 | 0.053 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".