Patient and Public Involvement in Clinical Practice Guidelines
Bibliographic record
Abstract
Background. The role of patient and public involvement programs (PPIPs) in developing and implementing clinical practice guidelines (CPGs) has generated great interest. Purpose. The authors sought to identify key components of PPIPs used in developing and implementing CPGs. Data sources. The authors searched bibliographic databases and contacted relevant organizations. Study selection. In total, 2161 articles and reports were retrieved on PPIPs in the development and implementation of CPGs. Of these, 71 qualified for inclusion in the review. Data extraction. Reviewers independently extracted data on key components of PPIPs and barriers and facilitators to their operation. Data synthesis. Over half of the studies were published after 2002, and more than half originated from the United States, the United Kingdom, Australia, and Germany. CPGs that involved patients and the public addressed a variety of health problems, especially mental health and cancer. The most frequently cited objective for using PPIPs in developing CPGs was to incorporate patients’ values or perspectives in CPG recommendations. Patients and their families and caregivers were the parties most often involved. Methods used to recruit PPIP participants included soliciting through patient/public organizations, sending invitations, and receiving referrals and recruits from clinicians. Patients and the public most often participated by taking part in a CPG working group, workshop, meeting, seminar, literature review, or consultation such as a focus group, individual interview, or survey. Patients and the public principally helped formulate recommendations and revise drafts. Limitations. The authors did not contact the authors of the studies. Conclusion. This literature review provides an extensive knowledge base for making PPIPs more effective when developing and implementing CPGs. More research is needed to assess the impact of PPIPs and resources they require.
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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 | 0.174 | 0.443 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".