Patient and public involvement in clinical guidelines: international experiences and future perspectives
Bibliographic record
Abstract
BACKGROUND: Clinical practice guidelines (CPG) are important tools for improving patient care. Patient and public involvement is recognised as an essential component of CPG development and implementation. The Guideline International Network Patient and Public Involvement Working Group (G-I-N PUBLIC) aims to support the development, implementation and evaluation of guideline-oriented patient and public involvement programmes (PPIPs). OBJECTIVE: To develop an international practice and research agenda on patient and public involvement in CPG. METHOD: 56 CPG developers, researchers, and patient/public representatives from 14 different countries, were consulted in an international workshop. Recommendations were validated with G-I-N PUBLIC steering committee members. RESULTS: Many CPG organisations have set up PPIPs that use a range of participation, consultation and communication methods. Current PPIPs aim to improve the quality and responsiveness of CPGs to public expectations and needs, or to foster individual healthcare decisions. Some organisations use structured involvement methods, including providing training for patient and public representatives. A number of financial, organisational and sociopolitical barriers limit patient and public involvement. The paucity of process and impact evaluations limits our current understanding of the conditions under which patient and public involvement is most likely to be effective. CONCLUSION: Greater international collaboration and research are needed to strengthen existing knowledge, development and evaluation of patient and public involvement in CPG.
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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 | 0.065 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".