Referee report. For: How to incorporate patient and public perspectives into the design and conduct of research [version 1; referees: 1 approved, 1 approved with reservations]
Bibliographic record
Abstract
International government guidance recommends patient and public involvement (PPI) to improve the relevance and quality of research. PPI is defined as research being carried out ‘with’ or ‘by’ patients and members of the public rather than ‘to’, ‘about’ or ‘for’ them ( http://www.invo.org.uk/ ). Patient involvement is different from collecting data from patients as participants. Ethical considerations also differ. PPI is about patients actively contributing through discussion to decisions about research design, acceptability, relevance, conduct and governance from study conception to dissemination. Occasionally patients lead or do research. The research methods of PPI range from informal discussions to partnership research approaches such as action research, co-production and co-learning. This article discusses how researchers can involve patients when they are applying for research funding and considers some opportunities and pitfalls. It reviews research funder requirements, draws on the literature and our collective experiences as clinicians, patients, academics and members of UK funding panels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.340 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.021 | 0.017 |
| Insufficient payload (model declined to judge) | 0.415 | 0.302 |
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".