Patient Engagement In Research: Early Findings From The Patient-Centered Outcomes Research Institute
Bibliographic record
Abstract
Charged with ensuring that research produces useful evidence to inform health decisions, the Patient-Centered Outcomes Research Institute (PCORI) requires investigators to engage patients and other health care stakeholders, such as clinicians and payers, in the research process. Many PCORI studies result in articles published in peer-reviewed journals that detail research findings and engagement's role in research. To inform practices for engaging patients and others as research partners, we analyzed 126 articles that described engagement approaches and contributions to research. PCORI projects engaged patients and others as consultants and collaborators in determining the study design, selecting study outcomes, tailoring interventions to meet patients' needs and preferences, and enrolling participants. Many articles reported that engagement provided valuable contributions to research feasibility, acceptability, rigor, and relevance, while a few noted trade-offs of engagement. The findings suggest that engagement can support more relevant research through better alignment with patients' and clinicians' real-world needs and concerns.
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 | 0.276 | 0.437 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".