A knowledge synthesis of patient and public involvement in clinical practice guidelines: study protocol
Bibliographic record
Abstract
BACKGROUND: Failure to reconcile patient preferences and values as well as social norms with clinical practice guidelines (CPGs) recommendations may hamper their implementation in clinical practice. However, little is known about patients and public involvement programs (PPIP) in CPGs development and implementation. This study aims at identifying what it is about PPIP that works, in which contexts are PPIP most likely to be effective, and how are PPIP assumed to lead to better CPGs development and implementation. METHODS AND DESIGN: A knowledge synthesis will be conducted in four phases. In phase one, literature on PPIP in CPGs development will be searched through bibliographic databases. A call for bibliographic references and unpublished reports will also be sent via the mailing lists of relevant organizations. Eligible publications will include original qualitative, quantitative, or mixed methods study designs reporting on a PPIP pertaining to CPGs development or implementation. They will also include documents produced by CPGs organizations to describe their PPIP. In phase two, grounded in the program's logic model, two independent reviewers will extract data to collect information on the principal components and activities of PPIP, the resources needed, the contexts in which PPIP were developed and tested, and the assumptions underlying PPIP. Quality assessment will be made for all retained publications. Our literature search will be complemented with interviews of key informants drawn from of a purposive sample of CPGs developers and patient/public representatives. In phase three, we will synthesize evidence from both the publications and interviews data using template content analysis to organize the identified components in a meaningful framework of PPIP theories. During a face-to-face workshop, findings will be validated with different stakeholder and a final toolkit for CPGs developers will be refined. DISCUSSION: The proposed research project will be among the first to explore the PPIP in CPGs development and implementation based on a wide range of publications and key informants interviews. It is anticipated that the results generated by the proposed study will significantly contribute to the improvement of the reconciliation of CPGs with patient preferences and values as well as with social norms.
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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 | 0.154 | 0.201 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.066 | 0.009 |
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".