Recommendations for patient engagement in guideline development panels: A qualitative focus group study of guideline-naïve patients
Bibliographic record
Abstract
BACKGROUND: Patient and consumer engagement in clinical practice guideline development is internationally advocated, but limited research explores mechanisms for successful engagement. OBJECTIVE: To investigate the perspectives of potential patient/consumer guideline representatives on topics pertaining to engagement including guideline development group composition and barriers to and facilitators of engagement. SETTING AND PARTICIPANTS: Participants were guideline-naïve volunteers for programs designed to link community members to academic research with diverse ages, gender, race, and degrees of experience interacting with health care professionals. METHODS: Three focus groups and one key informant interview were conducted and analyzed using a qualitative descriptive approach. RESULTS: Participants recommended small, diverse guideline development groups engaging multiple patient/consumer stakeholders with no prior relationships with each other or professional panel members. No consensus was achieved on the ideal balance of patient/consumer and professional stakeholders. Pre-meeting reading/training and an identified contact person were described as keys to successful early engagement; skilled facilitators, understandable speech and language, and established mechanisms for soliciting patient opinions were suggested to enhance engagement at meetings. CONCLUSIONS: Most suggestions for effective patient/consumer engagement in guidelines require forethought and planning but little additional expense, making these strategies easily accessible to guideline developers desiring to achieve more meaningful patient and consumer engagement.
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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 | 0.080 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".